Women-Owned Businesses and Access to Bank Credit: Evidence from Three Surveys Since 1987

Venture Capital, Vol. 8, No. 1, 51 67, January 2006 Women-Owned Businesses and Access to Bank Credit: Evidence from Three Surveys Since 1987 MONICA ZIMMERMAN TREICHEL & JONATHAN A. SCOTT Temple University, Fox School of Business and Management, Philadelphia (Accepted 30 September 2005) ABSTRACT Women-owned businesses are often thought to face difficulties in applying for and securing bank loans (Schwartz, 1979; Riding and Swift, 1990; Buttner and Rosen, 1992; Fabowale et al., 1995; Haines et al., 1999; Coleman, 2000). While there may always be individual instances of difficulties with credit availability that might receive the attention of the media, the more important issue especially given the increasing contribution of women-owned business to growth in the US economy, is whether women-owned businesses face any systemic, non-economic discrimination in applying for credit. We test three questions related to the success of women-owned businesses in accessing commercial bank financing. First, are women-owned businesses less likely to apply for bank loans than businesses owned by men? Second are womenowned businesses more likely to be turned down in their most recent loan application? And finally, if approved on their most recent loan application, are they more likely to receive a smaller loan? We found gender to be related to the application for bank loans as well as the size of the loans but not to the frequency of turndowns. These findings may be due to an omitted variable that could capture women s concerns about maintaining control over their business. KEY WORDS: Women-owned business, small business, credit availability Introduction The role of small businesses in generating employment growth in the US economy has been well established. 1 Small firms produce 51% 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 US that represent an increasing share of the overall small business contribution to US 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 in US sales and Correspondence Address: Monica Zimmerman Treichel, Temple University, Fox School of Business and Management, Strategic Management Department, 1810 North 13th Street, 380 Speakman Hall 006-00, Philadelphia, Pennsylvania, PA 19122, USA. Tel: þ1 (215) 204 6876; Fax: þ1 (215) 204 8029; Email: monica.treichel@temple.edu ISSN 1369-1066 Print/1464-5343 Online/06/010051-17 Ó 2006 Taylor & Francis DOI: 10.1080/13691060500453726

52 M. Z. Treichel & J. A. Scott 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 businesses, regardless of the owner s gender, bank lending is the most important overall source of external funding (Berger and Udell, 2002a), and for women-owned businesses, some have argued that securing bank loans is difficult (Chaganti et al., 1995; Coleman, 2000). In 2004, 48% of all privately held firms were women-owned businesses. Between 1997 and 2004, compared to all businesses, the estimated growth in the number of women-owned businesses was nearly twice the rate, employment expanded at twice the rate, and estimated revenues were slightly higher (www.womensbusinessresearch.org/ topfacts.html). This increasing contribution of women-owned businesses to the growth of the US economy, juxtaposed with the dramatic consolidation of the US banking system, makes it important for us to address the difficulties faced by women-owned businesses. Although there is a growing body of literature addressing the financing of women-owned businesses, not enough is known about the attempts of such businesses to access bank loans and their success in securing those loans (Fabowale et al., 1995), and this warrants further investigation. While there is a common belief that women-owned businesses face discrimination in securing loans from banks (Schwartz, 1979; Riding and Swift, 1990; Buttner and Rosen, 1992; Fabowale et al., 1995; Haines et al., 1999; Coleman, 2000), after controlling for the firm size, years in business, industry, and other owner/firm characteristics, discrimination does not appear to exist. More importantly, virtually all of the studies rely on a crosssectional examination of a sample of borrowers at one point in time. While these results may be generalized, market conditions, bank structure and competition change over time. In this paper, we use a unique set of data to examine the ability of womenowned businesses to access debt markets. The data consist of survey responses of small business owners from the same national trade organization at three different points in time: 1987, 1995 and 2001. This period encompasses not only the rapid growth in women-owned businesses, but also a dramatic change in the US banking markets. The number of banking organizations shrank from almost 14 000 to 8 000, which could portend a problem for women-owned business credit availability (www.fdic.gov/hsob). Yet during this period about 100þ new bank charters per year were granted and these de novo banks typically have a strong small business lending orientation. Also during this period some of the nation s largest regional banks began a strong emphasis on marketing to midmarket and small businesses. The three independent surveys provide an opportunity to investigate how this ferment in the banking industry affected banking outcomes for women-owned businesses an analysis that cannot be completed using a cross-sectional database at only one point in time. 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 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 women-owned businesses able to secure loans of comparable size?

Access to Bank Credit 53 We begin with a review of literature on the efforts of women-owned businesses to finance the 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 Governors of the Federal Reserve System. Berger and Udell (2002a), using data from the 1993 Survey of Small Business Finance, report that the average US small business relies equally on debt and equity financing. Among equity sources, the largest category is insider funds or retained earnings (31%), followed by other equity (13%) 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%). Among debt sources, commercial banks are the largest source (19% of the total or 38% of all debt financing), followed by trade credit (16% of the total or 21% 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% of the total. 4 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 et al., 1991; Coleman, 2000). When they do secure debt funding, it is most often from savings and loans, family members or banks (Schwartz, 1979; Coleman, 2000). The dominant sources of funding used by womenowned businesses are earnings from the business, savings, home equity loans, credit cards and family loans. They are significantly less likely to use bank loans than men to finance their business (Coleman and Carsky, 1996). Some have argued that securing debt funding, especially from banks, is difficult for women-owned businesses (Chaganti et al., 1995) and access to capital appears to be the greatest problem concerning women-owned businesses (Orser et al., 2000). In the study of debt funding through commercial bank loans, there is a common belief that women-owned businesses face discrimination (i.e. non-economic factors that affect access to bank financing) in accessing bank loans (Schwartz, 1979; Buttner and Rosen, 1989, 1992; Riding and Swift, 1990; Fabowale et al., 1995; Orser et al., 2000). The belief that such discrimination exists may prevent women-owned businesses from applying for loans (Coleman, 2000) and may limit the size of the loan applied for by women-owned businesses compared to those owned by men. Both of these may limit the growth opportunities for women-owned businesses (Haines et al., 1999). If discrimination exists, the rate of approved loans to womenowned businesses compared to those owned by men may be affected, although an analysis of differences in loan approvals of men- and women-owned businesses must control for differences in application frequency. Some evidence of discrimination has been found. Women-owned businesses were found to pay higher interest rates and had higher collateral requirements than

54 M. Z. Treichel & J. A. Scott businesses owned by men (Riding and Swift, 1990; Coleman, 2000), and were given larger counter offers than those given to men (Buttner and Rosen, 1989). Womenowned businesses were found to experience a higher incidence of unmet credit needs (i.e. turned down on their most recent loan or did not apply for fear of being turned down) (Cavaulluzzo et al., 2002) and reported less satisfaction with lending terms than men (Orser et al., 1994; Fabowale et al., 1995). Fay and Williams (1991) found that although there were no differences in the resources and experience between men and women applicants, loan officers were less likely to offer loans to women because the women had insufficient equity or security. Buttner and Rosen (1988) found characteristics of successful entrepreneurs were more frequently attributed to men than to women by bank loan officers suggesting that sex stereotypes may influence bank lending. According to Stevenson (1986), women have been denied access to capital because they have traditionally and historically been confined to domestic roles. Similarly, Fay and Williams (1991) argued that criteria used by lenders may discriminate against women business owners because women experience greater difficulties in acquiring the skills and knowledge necessary to conform to the criteria (e.g. women s work experience, education, socialization, etc. do not provide them opportunity to accumulate sufficient assets). Some empirical research, however, contradicts the presence of discrimination (Buttner and Rosen, 1989, 1992; Riding and Swift, 1990; Orser et al., 1994; Fabowale et al., 1995; Haines et al., 1999; Coleman, 2000). Buttner and Rosen (1989) found no evidence of sex stereotypes in the funding decisions of loan officers male entrepreneurs were not favoured over female entrepreneurs in funding decisions. Buttner and Rosen (1992) found no significant gender difference in the perceptions of difficulty in obtaining a loan for a new venture and attributions about the underlying reasons for rejection. They did find that women respond differently to rejection women were more likely to put their entrepreneurial plans on hold and were more likely to pursue venture capital after rejection than men. With regard to loan applications, Robb and Wolken (2002) found the business owner s characteristics and credit history as well as business risk, but not gender, prevented business owners from applying for a loan for fear of denial. Haines et al. (1999) found that after controlling for firm size, age and sector, gender is not associated with the loan size or interest rate charged and no greater collateral was demanded, and Uzzi (1999) found no significant differences in the cost of capital of loans to women- and men-owned businesses. It appears that differences in the application for, approval of, and size of loans to women-owned businesses and men-owned businesses may be due to differences in business characteristics such as size, age, risk and industry rather than to gender (Riding and Swift, 1990; Orser et al., 1994; Fabowale et al., 1995; Haines et al., 1999; Robb and Wolken, 2002; Storey, 2004). 5 Research indicates that women-owned businesses are typically smaller than those owned by men (Riding and Swift, 1990; Orser et al., 1994; Fabowale et al., 1995; Coleman and Carsky, 1996; Coleman, 2000; Orser et al., 2000). 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; Storey, 2004) and pay higher interest (Brau, 2002) than larger businesses. Fabowale et al. (1995) argued that although gender is not a determinant of the credit terms, it is highly

Access to Bank Credit 55 correlated with the size of the business women-owned businesses are typically smaller, have less capacity, less capital, a narrower range of collateral, and an unproven track record/character relative to businesses owned by men. This may have an adverse effect on the perceived capacity of women to service or to repay their loans, and so they may face greater difficulty in obtaining credit. Smaller firms are less cost-efficient and possibly more risky, and so banks incur greater costs to evaluate and monitor small businesses. For small loans, the profit margin is not justified by the costs, and so banks may decline loans to small businesses or make the terms more stringent (Orser et al., 1994; Haines et al., 1999). Size has been argued to influence the approval and terms of loans to women-owned businesses (Orser et al., 1994; Coleman, 2000). Coleman (2000) concluded that women-owned businesses may face a size disadvantage: women-owned businesses are typically smaller and lenders discriminate based on size, and so women-owned businesses obtain credit under less favourable terms than do businesses owned by men. Although Haines et al. (1999) did not find a statistical difference in the number of employees of women- and men-owned firms that secured loans, they found that the women-owned businesses had lower levels of liabilities and sales than businesses owned by men. 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. Womenowned businesses were found to be significantly younger than those owned by men (Riding and Swift, 1990; Coleman and Carsky, 1996; Haines et al., 1999; Coleman, 2000; Robb and Wolken, 2002), and Brau (2002) found older firms paid a significantly lower interest rate than younger firms. Robb and Wolken (2002) found that women-owned businesses had significantly shorter relationships with their lending institutions than did businesses owned by men, which may be due to the younger age of women-owned businesses. Because the strength of the lending relationship affects the price and availability of credit (Uzzi, 1999; Berger and Udell, 2002a; Brau, 2002), a shorter relationship with a lender may influence the application for and approval of loans, as well as loan terms. A third characteristic that may explain differences in bank lending to men- and women-owned businesses is the industry in which the business operates. Womenowned-businesses are more likely to operate in retail and services than businesses owned by men (Belcourt et al., 1991; Buttner and Rosen, 1992; Fabowale et al., 1995; Coleman and Carsky, 1996; Coleman, 2000; Robb and 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. However, Haines et al. (1999) found no significant gender difference in the industry in which the businesses operated among firms that secured loans. A fourth characteristic related to differences in lending is that of risk. Womenowned businesses are typically considered riskier than those owned by men (Riding and Swift, 1990; Fabowale et al., 1995; Coleman and Carsky, 1996; Robb and Wolken, 2002). Some of the dimensions of risk could include size, age and type of business, as mentioned above. Haines et al. (1999) found that women-owned firms are more frequently below the threshold of bankability that lenders require of borrowers women-owned businesses are known to be younger, smaller and operate in industry sectors such as service and retail, which explains a higher turndown rate among women owners. In addition, the growth rates of women-owned businesses

56 M. Z. Treichel & J. A. Scott were found to be less stable and lower than businesses owned by men (Riding and Swift, 1990; Fabowale et al., 1995). Coleman (2000) noted that if women-owned businesses are perceived as riskier than businesses owned by men, they may be denied credit or offered credit with less favourable terms. Haines et al. (1999) found, however, that lenders do not perceive women business borrowers to be more risky or problematic than men borrowers, and Coleman (2000) noted that the appearance of risk may decrease through a long-term relationship with lenders. Although some literature indicates there are differences between men- and womenowned businesses in bank loan applications, there is literature contradicting such differences. Uzzi (1999) found that women-owned businesses were less likely to apply for loans. Coleman (2000) argued that women-owned businesses might be less willing to seek external credit because the terms under which women typically obtain credit are less favourable than those given to men. 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 and Wolken, 2002). In summary, the evidence on whether women-owned businesses are disadvantaged in having access to commercial bank financing is contradictory. While some studies have found evidence of discrimination, a number of studies found that differences can be explained by business characteristics rather than the gender of the primary business owner(s). The conflicting results may be due to the samples and methodologies used in the previous studies. 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 2001. The use of three independent samples can be more valuable than a panel study when studying firm behaviour over a long period like we do. Assuming that you begin with a sample of new businesses, over a 14-year period, many (or most) of the companies would disappear through failure, and others would be lost due to non-response mortality. More importantly, the participation rate in starting firms and the types of businesses women own have surely changed over the past 15 years, as well as education levels. None of these changes would be captured in a panel study. And it would be virtually impossible to determine if other business environment changes such as the consolidation of the banking system had an effect on women-owned business access to credit because only the surviving businesses are responding in the later stages of the panel. Our three independent samples, which have many questions that look-back up to three years, adapt to these environmental changes better than a panel study. With these data, we are able to examine women-owned businesses experience with commercial bank credit using three outcomes. First we examine the likelihood of applying for bank loans, i.e. the number of owners applying to a bank for a loan divided by those applying to a bank and those that applied to no source for a loan. Businesses that apply to non-bank sources are excluded from the analysis, which has

Access to Bank Credit 57 a small effect on the number of observations but permits us to more clearly isolate any gender effect on the likelihood of applying to banks only. Next we examine the outcome of the most recent loan by evaluating the likelihood that the loan application at banks was turned down. Finally, we attempt to gauge the effect of gender on loan terms for successful applications. Relating the effect of gender to loan terms such as loan size, interest rate, collateral and compensating balance requirements is a complicated problem because banks can adjust along any of these margins to achieve their expected rate of return on the loan. For example a lower rate could be offset by a lower loan size or higher collateral/ compensating balance requirements. Instead of jointly estimating the effect of gender simultaneously on all elements that affect the bank s total return on the loan, we take a more modest approach and only examine the effect of gender on loan size. As such, we must be careful in the interpretation of any effect that might be found because a significant association between loan size and gender may reflect differences in preference for debt and not any adjustment by banks to achieve a total rate of return on their 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 2001. There were 1 921 respondents in the 1987 survey, 3 642 in the 1995 survey and 2 223 in the 2001 survey. For each survey, the questionnaire was mailed twice within a two-week interval to the random sample of members and duplicate responses were eliminated. The response rates have varied over time: 26% in 1987, 20% in 1995 and 18% in 2001. Although the response rates are low, they are comparable to those experienced by the Board of Governors of the Federal Reserve System s Survey of Small Business Finance. 6 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), where a commissioned telephone survey of small firms drawn from the Dun and Bradstreet files is used. Neither the NFIB nor the SSBF surveys attempt to verify the accuracy of the self-reported data. 7 A brief summary of the key firm demographic distributions for each survey is shown in the Appendix. A recent analysis of other NFIB surveys suggests that the profile of the respondents does not significantly differ from the overall membership from which the survey is drawn. 8 Thus, there is little reason to believe that there is any systematic self-reporting bias in the responses to the survey questions. The characteristics of the NFIB membership do show some differences from those of all small businesses. A comparison of the 1995 and 2001 distribution of employment, region and industry to the 1997 US Bureau of Census Enterprise Survey shows that the NFIB firms are larger than average, more frequently located in the Great Lakes and Plains regions, and more likely to be in manufacturing and less likely to be in service industries. 9 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 by gender.

58 M. Z. Treichel & J. A. Scott Results A profile of the respondents to each survey is presented in Table 1 for all firms. In addition, the distributions are reported separately for women-owned and menowned businesses. From 1987 to 2001, the proportion of women-owned businesses in the surveys increased slightly over time from 10% in 1987 to 12% in 2001. Womenowned businesses less frequently report applying for a loan, are more frequently turned down, and persistently borrow less than men-owned businesses. It is interesting to note that the turndown differences (i.e. the difference between the menand women-owned turndown rates) have narrowed over time, perhaps reflecting increased competition for this business segment. Women-owned businesses are more frequently organized as proprietorships and less frequently organized as corporations, 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. To test the likelihood of women-owned businesses applying for a bank loan, logistic regression is used to estimate the odds of applying for a bank loan. The dependent variable for each survey takes a value of 1 if the owner reports applying for a bank loan and 0 otherwise. 10 The key independent variables are gender of owner (women-owned, jointly owned, with men-owned the omitted variable), size of the business, years in business, recent sales growth, form of business, and 1-digit SIC industry classification. Table 2 presents the logistic model estimates of the odds ratio instead of the coefficient on each independent variable. An odds ratio above unity on the women-owned business variable should be interpreted as a woman-owned business is more likely than a male-owned business to apply for a loan, while an odds ratio less than unity would be less likely. The univariate differences in the frequency of women-owned businesses versus businesses owned by men in Table 1 are confirmed in the logistic regression analysis. Even after controlling for important business characteristics, women-owned businesses are significantly less likely to apply for a bank loan compared to businesses owned by men, and this outcome persists over time. 11 These results are economically meaningful as well. Using the mean value of the percentage of owners that applied for a loan in the 2001 survey, the probability of a woman-owned business applying for a loan at a bank is over 0.09 lower than the probability of a male-owned business applying. 12 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 menowned businesses. The dependent variable for this test is the outcome of the most recent loan application at a bank (1 ¼ applied and turned down; 0 ¼ applied and approved) and the same set of independent variables were used. 13 Location (MSA) and size of current bank (owner banks at a community financial institution, CFI) are also added as explanatory variables. Both location and bank size have been shown to be important in determining the turndown rate on loan applications (Scott, forthcoming). The logistic regression results, presented in Table 3, indicate that while womenowned businesses are more likely to be turned down (an odds ratio exceeding unity)

Access to Bank Credit 59 Table 1. Characteristics of respondents to the National Federation of Independent Business Credit, Banks and Small Business Surveys Survey date 1987 1995 2001 Firm characteristics All Female Male All Female Male All Female Male Owner gender Female 10 11 12 Male 75 69 66 Joint 14 20 19 Form of business Proprietorship 35 51 33 31 39 30 26 40 25 Partnership 8 7 7 6 6 6 8 7 8 S-corporation 21 18 20 24 16 24 Corporation 57 40 59 42 35 43 40 36 42 Industry Agriculture 4 2 4 8 6 8 7 4 8 Manufacture 13 8 13 13 9 14 15 8 13 Construction 11 6 12 13 6 15 12 2 18 Transportation 3 2 3 3 4 3 4 5 3 Wholesale 9 6 10 6 5 6 10 8 11 Retail 24 32 21 21 29 17 20 33 16 Finance 8 8 8 7 8 7 6 6 7 Services 21 33 19 21 23 20 15 21 14 Professional 8 3 9 5 6 5 7 8 7 Full-time equivalent employees (# FTE) 16.1 12.2 16.6 15.0 10.0 16.2 16.7 9.7 17.9 Years in business (years) 14.6 10.4 15.5 16.0 12.6 16.5 19.1 16.0 19.8 Most recent loan characteristics Applied for a loan at a bank 80 72 81 84 76 85 74 63 76 Turned down on most recent bank loan application 18 26 17 17 24 16 15 18 15 Amount of most recent bank loan ($000) $30.5 $17.3 $33.3 $54.8 $37.8 $57.9 $59.2 $33.4 $63.4 Notes: The frequency distribution is reported for the following variables: owner gender, form of business, industry, applied for loan and turned down on most recent loan request. The measurement units for the other variables are noted within the parentheses following the variable name.

60 M. Z. Treichel & J. A. Scott Table 2. Multivariate results of the decision to apply for a bank loan 1 1987 survey 1995 survey 2001 survey Independent variables exp(b) Std Err exp(b) Std Err exp(b) Std Err Female owned 0.703 0.136* 0.711 0.114** 0.612 0.100*** Jointly owned 1.128 0.223 0.853 0.119 1.073 0.163 Log FTE 1.454 0.121*** 1.659 0.127*** 1.592 0.117*** Log years in business 0.861 0.078* 1.009 0.073 1.431 0.178*** Sales growth 0.993 0.067 1.063 0.046 1.082 0.052* Proprietorship 0.614 0.091*** 0.964 0.135 1.047 0.158 Partnership 0.626 0.153* 0.900 0.218 0.862 0.179 S-corporation 0.955 0.148 1.055 0.159 Agriculture 1.596 0.631 2.128 0.572*** 1.701 0.433** Manufacturing 0.777 0.180 1.191 0.240 1.011 0.194 Construction 0.789 0.178 0.954 0.191 1.394 0.304 Transportation 0.656 0.239 1.011 0.336 1.659 0.575 Wholesale 0.919 0.246 1.546 0.448 1.310 0.289 FIRE 0.682 0.170 0.753 0.163 0.892 0.211 Business services 0.684 0.126** 0.806 0.126 1.169 0.214 Professional 1.437 0.412 1.654 0.523 1.049 0.252 No. of obs 1 702 3 243 1 920 LR chi-square 87.0 117.5 185.1 p-value 0.000 0.000 0.000 Adjusted r-squared 0.051 0.048 0.085 Notes: 1 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; the log odds ratio is presented for the coefficient estimates. The key independent variable is Female-owned, which takes a value of 1 if the respondent reports that the business is womenowned 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. ***indicates significance at the 1% level; **indicates significance at the 5% level and *indicates significance at the 10% level. on their most recent loan request in 1987 and 1995, this difference is not significant. Thus, if women-owned businesses more frequently report being turned down for a loan (as shown in Table 1), it appears that it is because of how size, legal form, firm age, growth, industry, size of current bank or location factor into the credit decision, but not because of gender. And, in somewhat of a surprise, women-owned businesses were less likely to be turned down in the 2001 survey, although again this difference is not significant. The lower odds ratio between the 1995 and 2001 survey is consistent however, with the declining difference in turndown rates reported in Table 1. Remarkably, this lack of association between gender and outcome of the most recent loan application at a bank has persisted through the dramatic change in bank structure during the survey periods. To test whether women-owned businesses, if approved on their most recent loan application, receive a lower loan amount (one element of the total return on a loan), we ran an ordinary least squares regression. The dependent variable for this test is the log of the loan size reported from the most recent successful application. In

Access to Bank Credit 61 Table 3. Multivariate results of the result of the last loan application at a commercial bank 1 1987 survey 1995 survey 2001 survey Independent variables exp(b) Std Err exp(b) Std Err exp(b) Std Err Female owned 1.234 0.290 1.265 0.195 0.897 0.237 Jointly owned 1.101 0.230 1.047 0.135 1.077 0.222 Log FTE 0.931 0.083 0.661 0.043*** 0.795 0.078** Log years in business 0.583 0.064*** 0.774 0.048*** 0.628 0.121** Sales growth 0.762 0.059*** 0.933 0.036* 1.000 0.065 Proprietorship 1.703 0.297*** 1.325 0.288 1.267 0.278 Partnership 1.472 0.426 1.051 0.134 1.027 0.329 S-corporation 0.876 0.119 0.746 0.161 Agriculture 0.849 0.356 0.708 0.158 0.701 0.257 Manufacturing 0.805 0.218 0.944 0.171 0.885 0.246 Construction 1.025 0.258 0.948 0.169 1.087 0.303 Transportation 0.756 0.362 1.730 0.467** 1.074 0.448 Wholesale 0.971 0.282 0.948 0.214 0.562 0.186* FIRE 0.878 0.283 0.899 0.207 0.433 0.203* Business services 1.025 0.220 1.364 0.200** 0.724 0.195 Professional 0.493 0.166** 0.746 0.189 0.822 0.303 Banks at CFI 0.699 0.105** 0.580 0.058*** 0.508 0.086*** MSA location 1.435 0.237** 1.499 0.165*** 1.054 0.103 No. of obs 1 362 2 884 1 321 LR chi-square 80.2 172.5 68.6 p-value 0.000 0.000 0.000 pseudo r-squared 0.062 0.061 0.062 Notes: 1 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 and the log odds ratio (exp(b)) are presented instead of the logistic regression coefficients. 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. ***indicates significance at the 1% level; **indicates significance at the 5% level and *indicates significance at the 10% level. addition to the previously used variables, a fourth set of independent variables related to loan terms is added to the equation. These terms include the collateral status (1 ¼ required), purpose (1 ¼ working capital needs), type of loan (1 ¼ revolving credit), and the log of the loan maturity. The results, presented in Table 4, indicate that women-owned businesses persistently report a smaller loan size compared to businesses owned by men, even after controlling for firm risk (i.e. size and years in business) and loan term variables as explanatory variables. Women-owned businesses persistently report a smaller loan size compared to businesses owned by men, although the effect is not significant in the 1995 survey. It is possible that the gender effect is not independent of size (log of FTE), i.e. women-owned businesses are demonstrably smaller and thus the negative association of loan size with gender may just be a firm size effect. To test this hypothesis, we created an interactive variable with gender and size (log of FTE) and

62 M. Z. Treichel & J. A. Scott Table 4. Multivariate results of the size of the most recent loan received 1 1987 survey (1) 1995 survey (2) 2001 survey (3) Independent variables Coef Std Err Coef Std Err Coef Std Err Female owned 70.344 0.125*** 70.153 0.104 70.411 0.146*** Jointly owned 70.150 0.102 0.026 0.076 70.126 0.109 Log FTE 0.596 0.040*** 0.623 0.034*** 0.681 0.047*** Log years in business 0.206 0.049*** 0.079 0.042* 0.062 0.110 Sales growth 70.022 0.037 70.011 0.023 0.054 0.036 Proprietorship 70.279 0.084*** 70.229 0.080*** 70.233 0.123* Partnership 70.026 0.137 70.022 0.141 0.255 0.171 S-corporation 0.187 0.077** 0.027 0.101 Agriculture 0.513 0.176*** 0.665 0.115*** 0.041 0.184 Manufacturing 70.027 0.123 0.018 0.104 70.117 0.148 Construction 0.060 0.118 0.262 0.104** 70.158 0.154 Transportation 0.008 0.199 0.247 0.173 0.424 0.242* Wholesale 0.092 0.131 0.500 0.132*** 0.235 0.161 FIRE 0.030 0.147 0.238 0.142* 0.137 0.200 Business services 70.245 0.106** 70.307 0.099*** 70.006 0.149 Professional 70.080 0.133 0.074 0.148 70.287 0.188 Banks at CFI 70.010 0.069 70.032 0.061 70.037 0.087 MSA location 70.004 0.080 0.038 0.063 70.017 0.056 Collateral required 0.756 0.073*** 0.451 0.064*** 0.511 0.090*** Purpose: working cap 0.099 0.072 0.250 0.075*** 0.024 0.096 Type: revolving credit 70.205 0.083** 70.387 0.075*** 70.114 0.122 Log of maturity 0.127 0.018*** 0.399 0.033*** 0.369 0.052*** Constant 0.633 0.238*** 0.780 0.221*** 0.564 0.369 No. of obs 1 254 1 696 989 Adjusted R-squared 0.377 0.388 0.346 F-test 30.2 36.8 16.9 p-value 0.000 0.000 0.000 1 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. ***indicates significance at the 1% level; **indicates significance at the 5% level and *indicates significance at the 10% level.

Access to Bank Credit 63 re-estimated 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. 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 men-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-owned businesses after controlling for business characteristics (Riding and Swift, 1990; Orser et al., 1994; Fabowale et al., 1995; Haines et al., 1999; Robb and Wolken, 2002; Storey, 2004), we found that gender does appear to be related to some aspects of access to bank financing. Specifically, gender is significantly related to the application for bank loans as well as the size of the loans, and these effects persist over time. In testing the likelihood of women applying for bank loans, we found that even after controlling for important business characteristics, women-owned businesses are significantly less likely to apply for a bank loan. This relationship was consistent over the three time periods studied. The lower likelihood of women-owned businesses applying for bank loans may be related to the belief held by women-owned businesses that they might face discrimination in the lending process. Concern for discrimination may prevent women-owned businesses from applying for bank loans. 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. Unfortunately we do not have any proxies for concern about monitoring and control by banks that would permit us to see if gender still influences the decision to apply for a loan. 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. This outcome is not surprising given the legal requirement prohibiting gender discrimination in lending. 14 Our results suggest that there is no likely difference in the approval of loans to women- and men-owned businesses after controlling for firm level characteristics. This finding was consistent over the three time periods we studied, and 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, legal form, age, growth, industry, size of current bank, and location account for differences in the approval/denial of a bank loan.

64 M. Z. Treichel & J. A. Scott In examining whether or not women-owned businesses are able to secure loans of comparable size to those secured by businesses owned by men, gender was found to be significant. Women-owned businesses reported a smaller loan size compared to loans reported by businesses owned by men, even after adding both business and loan term variables as explanatory variables. This outcome may 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 towards external funding, specifically debt funding. Thus, if a woman-owned business requires external funding, it may minimize the amount of funds it secures. This study has implications for women-owned businesses and for banks. First, women-owned businesses that do not apply for a loan for fear of being turned down should note that there is no difference in turndown rates between men- and womenowned businesses that applied for loans, despite the dramatic consolidation of the US banking system. However, these owners should be aware that if their business is younger and/or smaller, their business is more likely to be turned down, regardless of gender. Second, banks may need to do a better job of explaining how loan covenants work, especially as they relate to the bank s rights if a business faces financial difficulty. The lower frequency of loan application and the lower loan size for women-owned firms that persists across the three surveys cannot be explained by firm characteristics. These results may be attributable to concerns women business owners have about control of their business. Given that many women start or buy businesses to have greater control of their life, they may be less likely to seek external funding of their business to prevent losing control of the business. Thus, rather than discrimination, these outcomes may be the result of self-selection by women owners. If banks could better explain why they periodically need information about the business and how the business could use bank resources/references to improve financial controls, the business owner may be less concerned about surrendering (or the appearance of surrendering) control of the business to the lender. There are some limitations of this study that suggest avenues for future research. First, our data set does not include a variable that can serve as a proxy for the ownership control concerns of women-owned businesses as mentioned in the preceding paragraph. Without including this proxy variable, we cannot rule out noneconomic discrimination in application rates and loan size. Second, women-owned businesses may not apply for loans out of fear that their loan will be turned down, and our data set does not include a variable that can proxy such fear of turndown. Third, we do not address the influence of the relationship between women-owned businesses and their bank (relative to others) on the likelihood for loan application. Future research may benefit from understanding if, for example, women-owned firms that have been with their banks longer and are more comfortable with their banking relationship are more likely to apply for a loan. And, if women-owned businesses have preferences in a banking relationship that are not being met by banks, does this lead to lower rates of application and/or lower loan amounts? These important issues remain for future research.

Access to Bank Credit 65 Notes 1 See www.sba.gov/advo for the latest statistics on the contributions of small businesses to the US economy. 2 A women-owned business is defined as a business in which the principal owner(s) is (are) a woman (women). 3 The other sources of debt include other financial institutions, other business loans, owner loans, credit card, and other individual loans. Together these sources comprise 16% of total external financing or 31% of total debt financing. 4 For more information on the types of financing explained above, see Berger and Udell (2002b). 5 For a summary of studies on gender and commercial lending, see Fabowale et al. (1995). Appendix 1 includes the objectives, methodology, findings, conclusions, comments and limitations of each study. 6 A similar decline in the response rate has been experienced with the Board of Governors Survey of Small Business Finance. 7 The Fed collects a significant amount of income statement and balance sheet data that is cross-checked for consistency through internally developed algorithms. 8 See Small Business Administration Contract # SBAHQ-04-M0450, The Effect of changes in Monetary Policy on the Expectations, Spending Plans and Hiring Decisions of Small Business Owners (September 2005) for further detail. For example, in July 2001, 21% of the surveys distributed were returned. The differences in the industry distribution of the respondents to the industry distribution of the sample were statistically negligible based on standard sampling errors. 9 The Appendix provides a comparison to the 1997 Bureau of Census Enterprise Survey results for total employment, region and industry. 10 As noted above, applications made to non-bank sources are excluded in the analysis, which reduces the sample size by about 1900 observations in 2001, 3200 observations in 1995 and 1700 observations in 1987. 11 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, which confirms our prediction that representativeness should not have an effect on the correlations of interest in this study. 12 This approximation is obtained by taking the log of the odds ratio and multiplying it by p6(1 p) where p is the mean percentage of firms applying for a loan at a commercial bank. In this instance, ln (0.612)60.746(1 0.74) ¼ 70.094. 13 The estimates in Table 3 may suffer from a selection bias. This bias could occur if some unobserved characteristic that 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. 14 US Federal Reserve Regulation B implements Title VII of the Consumer Credit Protection Act (Equal Credit Opportunity Act). References Belcourt, M., Burke, R. and Lee-Gosselin, H. (1991) The glass box: women business owners in Canada, Background Paper, Canadian Advisory Council on the Status of Women, Ottawa. Berger, A. N. and Udell, G. F. (2002a) 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, pp. 613 673. Berger, A. N. and Udell, G. F. (2002b) Small business credit availability and relationship lending: the importance of bank organisational structure, Economic Journal, 112, pp. F32 F53. Brau, J. C. (2002) Do banks price owner-manager agency costs? An examination of small business borrowing, Journal of Small Business Management, 140(4), pp. 273 286. Buttner, E. H. and Rosen, B. (1988) Bank loan officers perceptions of the characteristics of men, women, and successful entrepreneurs, Journal of Business Venturing, 3(3), pp. 249 258.

66 M. Z. Treichel & J. A. Scott Buttner, E. H. and Rosen, B. (1989) Funding new business ventures: are decision makers biased against women entrepreneurs?, Journal of Business Venturing, 4(4), pp. 249 261. Buttner, E. H. and Rosen, B. (1992) Rejection in the loan application process: male and female entrepreneurs perceptions and subsequent intentions, Journal of Small Business Management, 30(1), pp. 58 65. Cavalluzzo, K. S., Cavalluzzo, L. C. and Wolken, J. D. (2002) Competition, small business financing, and discrimination: evidence from a new survey, Journal of Business, 75(4), pp. 641 679. Chaganti, R., DeCarolis, D. and Deeds, D. (1995) Predictors of capital structure in small ventures, Entrepreneurship Theory and Practice, 20(2), pp. 7 18. 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), pp. 37 52. Coleman, S. and Carsky, M. (1996) Understanding the market of women-owned small businesses, Journal of Retail Banking Services, 18(2), pp. 47 49. Fabowale, L., Orser, B. and Riding, A. (1995) Gender, structural factors, and credit terms between Canadian small businesses and financial institutions, Entrepreneurship Theory and Practice, 19(4), pp. 41 65. Fay, M. and Williams, L. (1991). Sex of applicant and the availability of business start-up finance, Australian Journal of Management, 16(1), pp. 65 72. Haines, G. H. Jr, Orser, B. J. and Riding, A. L. (1999) Myths and realities: an empirical study of banks and the gender of small business clients, Canadian Journal of Administrative Sciences, 16(4), pp. 291 307. http://app1.sba.gov/faqs/ (accessed on 3/31/05). Orser, B., Hogarth-Scott, S. and Riding, A. (2000) Performance, firm size, and management problem solving, Journal of Small Business Management, 38(4), pp. 42 58. Orser, B., Riding, A. and Swift, C. (1994) Banking experiences of Canadian micro-businesses, Journal of Enterprising Culture, 2(1), pp. 1 10. Riding, A. and Swift, C. (1990) Women business owners and terms of credit: some empirical findings of the Canadian experience, Journal of Business Venturing, 5(5), pp. 327 340. Robb, A. and Wolken, J. (2002) Firm, owner, and financing characteristics: differences between femaleand male-owned small businesses, Unpublished manuscript. Schwartz, E. B. (1979) Entrepreneurship: a new female frontier, Journal of Contemporary Business, 4, pp. 47 76. Scott, J. A. (forthcoming) Loan officer turnover and small business credit availability, Journal of Small Business Management. Stevenson, L. A. (1986) Against all odds: the entrepreneurship of women, Journal of Small Business Management, 24(3), pp. 30 36. Storey, D. J. (2004) Racial and gender discrimination in the micro firms credit market? Evidence from Trinidad and Tobago, Small Business Economics, 23, pp. 401 422. Uzzi, B. (1999) Embeddedness in the making of financial capital: how social relations and the networks benefit firms seeking financing, American Sociological Review, 64, pp. 481 505. www.womensbusinessresearch.org/topfacts.html (accessed on 8/26/05). www.fdic.gov/hsob (accessed on 3/27/05). www.sba.gov/advo (accessed on 3/25/05).

Access to Bank Credit 67 Appendix NFIB Survey characteristics versus 1997 US Census Bureau Enterprise Distributions NFIB Census Employment 1987 1995 2001 1997 0 4 31% 29% 40% 59% 5 9 30% 30% 25% 18% 10 19 20% 20% 17% 11% 20 and up 19% 21% 18% 12% Total 100% 100% 100% 100% Region New England 14% 15% 16% 21% South Atlantic 13% 12% 11% 18% Great Lakes 17% 21% 24% 16% Plains 12% 13% 14% 7% Mid-south/Southwest 16% 14% 13% 15% Mountain 10% 10% 10% 7% Pacific 18% 15% 12% 16% Total 100% 100% 100% 100% Industry Agriculture NA NA NA NA Construction 12% 15% 17% 12% Manufacturing/mining 13% 14% 13% 6% Transportation/comm. 3% 4% 4% 4% Wholesale 9% 7% 11% 8% Retail 25% 24% 22% 19% FIRE 8% 8% 7% 8% Services 30% 29% 25% 40% Total 100% 100% 100% 98% Notes: This table compares the distribution of responses by employment, region and 1-digit SIC classification for the National Federation of Independent Business (NFIB) 2001 Credit, Banks and Small Business Survey to the 1997 US Census Bureau Enterprise Survey (Census). The computation of the NFIB frequency distribution excludes no answer responses and thus will differ from the distributions shown in Table 1. Agriculture firms are excluded from the NFIB distributions because the Census survey is of non-agricultural firms.