The Differences in Financial Structure Between Women - Springer Link

The Differences in Financial Structure Between Women- and Men-Owned Family Businesses George W. Haynes Montana State University Barbara R. Rowe Utah State University Rosemary Walker Michigan State University Gong-Soog Hong Purdue University

ABSTRACT: Lenders often are faced with the challenge of evaluating the financial success of a business or a proposed business by examining the financial records of the George W. Haynes is Associate Professor in the Department of Health and Human Development at Montana State University, Bozeman, MT 59717. His current research interests include debt structure issues, the intermingling of family and business resources, and the impact of public support programs on Native American Business development. He may be contacted by email at: haynes噝montana.edu. Barbara R. Rowe is Professor and family and resource management extension specialist at Utah State University, Logan, UT. Her research interests include women’s roles in acquiring, financing, and managing family-owned businesses and bankruptcy education. Rosemary Walker is Professor at Michigan State University, East Lansing, MI. Her research interests are related to home-based businesses and small family-owned businesses. Gong-Soog Hong is Associate Professor and graduate director in the Department of Consumer Science and Retailing at Purdue University, West Lafayette, IN. Her research interests include families owning businesses, economic well-being of older individuals, health care issues, and tourism consumption analysis. This paper reports results from the USDA Cooperative Regional Research Project, NE-167R, “Family Businesses: Interaction in Work and Family Spheres,” partially supported by the Cooperative States Research, Education and Extension Service (CSREES), and the Agricultural Experiment Stations of Hawaii, Illinois, Indiana, Iowa, Michigan, Minnesota, Montana, Nebraska, New York, North Dakota, Ohio, Pennsylvania, Texas, Utah, Vermont, Wisconsin, and the Social Sciences and Humanities Council of Canada (SSHRC). Journal of Family and Economic Issues, Vol. 21(3), Fall 2000 䉷 2000 Human Sciences Press, Inc.

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household, reasoning that an assessment of the household’s financial position should be a plausible indicator of the financial status of the business. Utilizing data from the recently released Family Business Survey, this study uses financial information about both the family and the business to examine the relationship between household financial indicators and business financial indicators for women- and men-owned family businesses. The results suggest that, while household financial statements may be good indicators for men-owned businesses, they appear to be much less reliable for women-owned businesses. KEY WORDS: family businesses; women entrepreneurs; financing business ventures.

Introduction The years 1987 to 1997 could be known as “the decade of the woman entrepreneur.” The U.S. Small Business Administration’s Office of Advocacy estimates that during that, decade, the number of women-owned businesses rose by 89% to 8.5 million, increasing more steadily and more rapidly than any other small business segment in the economy. Women-owned businesses account for more than onethird of all businesses and generated $3.1 trillion in gross revenues over the last ten years, which represents an increase of 209%, even after adjusting for inflation. Some 23.8 million employees work for women-owned firms, which represents an increase of 262% over the 1987–1997 period (U.S. Small Business Administration, 1998). Although women-owned businesses are a vital and growing sector of the nation’s economy, previous research points to significant differences between women- and men-owned firms in gross revenues, industry location, and access to capital. (See Brush, 1992; Fay & Williams, 1993; Loscocco, Robinson, Hall, & Hall, 1991). These studies reported that women-owned businesses have significantly lower gross sales than men-owned businesses, although the factors influencing the success of the businesses are the same for women and men (Tigges & Green, 1994). Clark and James (1992) suggest that most women business owners would realize significantly higher earnings in management positions on payrolls than pursuing self-employment. Several reasons have been offered for the lower sales and income of women-owned firms. It has been proposed that women lack the human capital investments, such as education and managerial experience that self-employed men have (Aronson, 1991; Fischer, Reuber, & Dyke, 1993), that they are more conservative and risk-averse than men (Masters & Meier, 1988), that their businesses cluster into lowpaying industries (Loscocco et al., 1991), and that they manage their

G. W. Haynes, B. R. Rowe, R. Walker, and G.-S. Hong

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businesses to limit growth so that they do not jeopardize family and other commitments (Brophy, 1989; Hisrich & Brush, 1986). Others have hypothesized that women business owners are disadvantaged relative to men because women lack access to key resources and because they are not well integrated into business networks (Hagen, Rivchun, & Sexton, 1989; Tigges & Green, 1994). Several researchers have investigated whether women- and menowned businesses differ in profitability because women face differential access to financial credit (Ando, 1998; Buttner & Rosen, 1988, 1989; Clark & James, 1992; Fay & Williams, 1993; Haynes, 1995; Riding & Swift, 1990). Many women perceive that bankers discriminate against women business owners and that financial institutions view them and their businesses as a greater credit risk merely by virtue of their gender. Obtaining financing is difficult at every stage of business growth, but it is most problematic for small firms and start-ups. Small firms often are typified as firms with a high degree of information asymmetry. The asymmetry problem arises because the lender has limited information about the nascent entrepreneur and business venture. If lenders are unable to compile complete and reliable information about the entrepreneur and business, then they have the incentive to ration credit. Credit rationing occurs when either (a) among all loan applicants who appear to be identical, some receive a loan and some do not; and, (b) when there are identifiable groups of individuals, such as women business owners, in the population who are unable to obtain loans at any interest rate, even though with a larger supply of credit they would be given a loan (Stiglitz & Weiss, 1981). Lenders examine household financial documents when deciding whether or not to loan money to a small business owner. Household financial statements provide essential information to the lender in assessing the financial resources available for use as collateral and the ability of the business owner to manage debt. By examining the assets, liability, equity, and income of the household, lenders have a reasonable indication of the financial resources available. Further examination of the household’s debt-to-equity ratio and cash flow enables the lender to assess the manager’s ability to handle debt and control the cash flow of the business. If household financial indicators are related positively to business financial indicators, then lenders have reliable information about the business owner, and the degree of information asymmetry is minimized. If the degree of information asymmetry is minimized, then the probability of the borrower being

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subject to credit rationing approaches zero. The primary objective of this paper, therefore, is to assess the relationship between the financial structures of the family and business for women- and men-owned family business borrowers.

Review of Literature Gaining access to capital and credit is a critical challenge for both women and men business owners, but some previous research indicates that women encounter greater obstacles than men (Ando, 1988; Buttner & Rosen, 1988, 1989; Clark & James, 1992; Fay & Williams, 1993; National Foundation for Women Business Owners, 1993; Riding & Swift, 1990). Some of the difficulties for women entrepreneurs have been attributed to their lack of credit history, inadequate management or financial experience, and the lack of a business track record (Starr & Yudkin, 1996). In addition, problems in financing also may be due to the types of businesses typically owned by women: sole proprietorships, service businesses, firms with insufficient collateral and limited equity, and business in highly competitive or low-profit margin industries. Since many women-owned firms are still in early development, they have not yet reached the stage where venture or equity financing is appropriate. Women tend to invest less start-up capital than men. A report by the Small Business Administration shows that the average women business owner started her business with less than $5,000 (U.S. Small Business Administration, 1992). Women are also more likely to use private sources and credit cards for short-term financing. In an analysis, which compared women-owned firms to all firms in Dun and Bradstreet’s for-profit business database, 33% of women business owners used personal savings, family, and friends for short-term capital, compared with 10% of all small businesses. Fifty-two percent of women business owners used credit cards for financing compared with 18% of all small businesses (National Foundation for Women Business Owners, 1993). Since small businesses rely heavily on bank financing for both short- and long-term capital, most of the research on financing has focused on this source. In 1994, women-owned firms received only 14% of the $6.4 billion in loans guaranteed by the Small Business Administration (U.S. Small Business Administration, 1994). In surveys of women business owners, many women report that financial


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institutions either refuse their applications for credit or, if the loans are approved, demand higher levels of collateral, charge higher interest rates, and more frequently require co-signers on loans and lines of credit (Starr & Yudkin, 1996). However, the studies that have tested these claims of discrimination are inconclusive. Recent work by Haynes and Haynes (1999) suggests that women- and men-owned businesses have very similar access to credit from commercial banks (especially access to line of credit loans). However, women business owners are still more likely to borrow from friends and family. In an analysis of 1,300 business owners, Ando (1988) found no credit discrimination against Asian or Hispanic business owners, either women or men, although there appeared to be some discrimination against Black business owners of both genders and against divorced male business owners regardless of race. Clark and James (1992), using data from the Bureau of Census’s Economic Censuses, reported that access to capital was a problem for single women, who had fewer personal assets to invest than married women with access to home equity and a spouse’s paycheck. Most (89%) of the womenowned businesses in their study had started their businesses with less than $10,000. Haynes (1995), using data from the National Survey of Small Business Finance (NSSBF), examined the loan terms faced by womenand men-owned businesses to determine if there were gender differences in the cost of capital. He examined each loan type offered by each lender. While the mean values of interest rates appeared to differ, none of the comparisons reached statistical significance. To test the notion that women-owned businesses pay higher interest rates than men-owned businesses, a multivariate model controlling for credit risk, size, age, legal organization, standard industrial classification, rural or urban location, minority ownership, and financial market concentration was created. Based upon his model, using all lenders and loans in a sample of 2,302 businesses, women-owned businesses paid interest rates similar to men-owned businesses but appeared to receive smaller loan amounts. This was true even when controlling for the factors mentioned above (Haynes, 1995). He noted that women-owned businesses have a higher probability of resorting to non-institutional sources of financial capital, such as family and friends, and a lower probability of acquiring mainstream types of financing, such as line of credit loans (Haynes, 1995). Women may be disadvantaged in financial markets because they are more reluctant than men to apply for loans from lending institu-

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tions because they believed that they would be turned down. There may be some validity to this view. In a study of bank loan officers’ perceptions of male and female entrepreneurs, Buttner and Rosen (1988) found that women were evaluated significantly lower on the dimensions related to leadership, autonomy, risk-taking propensity, emotionalism, readiness for change, energy level, and need for support. These findings raise questions regarding the degree to which loan officers are influenced by gender stereotypes in evaluating loan applications for new businesses and the degree to which loan officers make it more difficult for women to succeed in new ventures. Studies which actually have examined women’s propensity for risk and the suitability of their managerial background or education have found more similarities between the sexes than differences (Chaganti, 1986; Goffee & Scase, 1985; Masters & Meier, 1988), and no study has indicated that women are less well suited to small business ownership than men (Loscocco et al., 1991). Riding and Swift (1990), in an effort to determine if differences in credit availability were due to gender or to the type of firms women initiated, matched a sample of women- and men-owned businesses on the basis of age, size, industry, growth rate, and organizational form of the business. They identified only one area in which statistically significant gender-related differences remained: collateral requirements to establish a line of credit were more difficult for women than for men. The relatively low profitability of women-owned businesses has given lenders the impression that their businesses have a lower probability of success, thus making financial capital more difficult for women business owners to acquire (Haynes, 1995). One further explanation that is offered for women’s lack of success in securing financial resources is that women lack access to key information networks from which to obtain advice and assistance. As Cromie and Birley (1992) point out, small business owners or managers are required to perform a countless number of novel and nonrecurring tasks for which they have little or no training. In these situations, support, friendly advice, or direct assistance from personal contacts can be extremely valuable in enabling them to acquire resources and exploit market opportunities. Tigges and Green (1994) did find gender differences in the use of formal and informal sources of business assistance in rural businesses, with men being more likely than women to use lawyers and certified public accountants for assistance and women being more likely than men to use informal sources, such as family or friends. Alternatively, Cromie and Birley (1992) re-


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ported that women entrepreneurs were remarkably similar to men in their general propensity to network, their networking activity and density, and the profile of their personal contact network. Whether it is because they start enterprises that need less capital or because they are less able to obtain credit, women entrepreneurs start out with far less debt than their male counterparts (Hisrich & Brush, 1987; Scherr, Sugrue, & Ward, 1993). This does not necessarily translate into a disadvantage because women owners may be better able to stay in business because they have less debt. However, the success of any business venture depends upon the entrepreneur’s ability to obtain financial support. If women’s businesses are to grow at the same rate as men’s, limiting the amount of debt they can access or offering it at higher costs impedes their ability to succeed (Zellner, King, Byrd, DeGeorge, & Birnbaum, 1994). This research attempts to answer the questions: Is the financial structure of the family a good indicator of the financial structure of the business for women- and men-owned family businesses? Does the answer vary by gender? The specific objectives were (a) to describe the personal and demographic characteristics of the household and the characteristics of the business related to the family businesses of women and men; (b) to compare the business and household financial indicators of women- and men-owned businesses; and (c) to compare the relationship between household financial indicators and business financial indicators, ceteris paribus, by gender.

Methods and Sample Sample Selection The data for this study were collected in two 30-minute telephone interviews from a national sample of 794 family businesses over a period of 12 months. For purposes of the study, a family business was defined as a business that was owned and managed by one or more family members (Hollander & Elman, 1988). To qualify for the study, the business needed to be in existence for at least one year and the owner-manager needed to be working at least 6 hours per week year round or a minimum of 312 hours per year in the business, needed to be involved in its day-to-day management, and needed to reside with another family member. These criteria excluded singleperson households and nonfamily households but did include households of opposite-sex and same-sex cohabitors. The sampling frame was any household in the 50 states with a listed telephone number. Data were collected in a two-stage procedure. A list of household telephone numbers, names, and ad-

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dresses was purchased from Survey Sampling, in Fairfield, Connecticut, which provides lists of phone numbers based upon specified sampling frames. During 1997, more than 14,000 households were called, and 9,896 households answered a 5-minute screening questionnaire designed to ascertain if they met the study’s criteria. This yielded 1,387 business-owning households, but 271 households were eliminated because they did not meet one of the following criteria: the length of time in business or hours worked per year. From the remaining households, 794 completed either a business manager interview or a household manager interview. Of these 794, 86 households refused to complete the business interview, and 35 refused to complete the household interview. In 259 eligible households, the household manager and the business manager were the same person. Missing data were imputed. For further details regarding sample selection and methodology, see Winter, Fitzgerald, Heck, Haynes, and Danes (1998). The sample selected for this study included the 414 households that completed both the household and business interviews and the 259 households in which the business and household manager were the same person. A subsample of 307 households classified as borrowers was selected. If total household debts were greater than zero and total business liabilities were greater than zero, the respondent was classified as a borrower.

Variable Construction The variables chosen for this study rely heavily on the previous work of Clark and James (1992), Tigges and Green (1994), Loscocco et al. (1991), and Haynes (1995). This study utilizes dependent variables relating to the financial structure of the business and household. The independent variables include the structural characteristics of the business and the personal and demographic characteristics of the household. Six financial variables relating to the business are the dependent variables. The log of total business assets, the log of total business liabilities, and the log of business profits were determined by respondents’ answers to questions about their total business assets and total business liabilities as of December 31, 1996, and their business profits in 1996. Business equity is computed by subtracting total liabilities from total business assets. Debt-to-equity ratio is computed by dividing total business debt by equity. From this ratio, a dummy variable was created: if the ratio is greater than 1, debt-to-equity is coded 1; otherwise, 0. The measure, cash flow problems, is a dummy variable in response to a question about how often the business had a cash flow problem in 1996. If there were several cash flow problems in 1996, it was coded 1; otherwise, 0. Similarly, the household financial variables are log of total household assets, log of total household debt, log of household net worth, household debtto-equity ratio, log of household income, and cash flow problems. Responses to questions regarding total household assets and total household debt as of December 31, 1996, and the household’s total income from all sources in 1996 provide the basis for the variables. Household net worth is assets minus debts. Household debt-to-equity ratio is a dummy variable which equals 1 if


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household debt divided by household net worth is greater than 1; otherwise, 0. “Cash flow problems” is a dummy variable created in response to a question concerning how often there was a cash flow problem in the household. If there were several cash flow problems in 1996, the variable was coded 1; otherwise, 0. Independent variables include business characteristics and household characteristics. Business characteristics are the number of employees, business age, experience of the business manager, legal organization of the business, and industrial classification of the business. Household characteristics include residential location, household size, presence of children in the household, age of the household manager, and gender of the household manager. The number of employees often is indicative of firm size. It is constructed by adding the number of full-time employees to one-half the number of parttime employees on the assumption that part-time employees work one-half as much as full-time employees. Business age is computed by subtracting the year the business began operating from 1997. Experience of the business manager is computed by subtracting the age at which the manager first starting working in this business from the manager’s age at the time of the interview. Legal organization of the business has four categories: sole proprietorship, partnership, subchapter S corporation, or general corporation, C. Industrial classification is coded into eight categories based upon the North American Industry Classification System (NAICS), a six-digit code system for classifying establishments by type of economic activity (U. S. Executive Office of the President, 1998). The eight categories are agriculture and mining; construction; manufacturing; transportation; wholesale trade; retail trade; finance, insurance, and real estate (FIRE); and professional services. Location applies to characteristics of the household, specifically to where the respondent lived at the time of the interview. This variable has three categories: (a) rural is defined as living on a farm; (b) in a rural area, but not on a farm; or (c) in a small town with population less than 2,500. Small town includes living in a town with a population of 2,500 to 50,000. Urban identifies the respondent as living in a city larger than 50,000. Household size is the response to a question about how many people currently were living in the household. Recall that to be included in the survey, the household (i.e., family) was required to consist of at least two people. The measure, children in the household, is a variable created from information about the ages of all members of the household. It is a dummy variable with a value of 1 if any members are less than 18 years; otherwise, 0. Age and gender of the household manager are the final two household characteristics variables. Table 1 lists the measurement of all dependent and independent variables.

Analysis To compare the means of the various household and business financial indicators by gender, the natural logarithms of household and business assets, liabilities, equity, and total income (i.e., business profits) were used in the simple regressions (Table 3). Ordinary least squares (OLS) regression was the

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TABLE 1 Measurement of Variables Variable DEPENDENT VARIABLES: Total business assets Total business liabilities Business profits Business equity High business debt Business cash flow problems INDEPENDENT VARIABLES: Household characteristics Total household assets Total household debts Total household net worth High household debt Household income Household cash flow problems Residence Rural Small town Urban Household size Presence of children Age of household manager Gender of household manager Business characteristics Number of employees Business age Experience of business manager Legal organization Sole proprietorship Partnership Subchapter S corporation General corporation

Measurement

Log of total business assets on Dec. 31, 1996 Log of total business liabilities on Dec. 31, 1996 Log of total business profits in 1996 Log of (total business assets—total business liabilities) 1 if (total business debt/business equity) ⬎ 1, 0 if otherwise 1 if the business had a cash flow problem in 1996, 0 if otherwise

Log of total household assets (home and other household assets) on Dec. 31, 1996 Log of total household debt including home mortgage on Dec. 31, 1996 Log of (total household assets—total household debts) 1 if (total household debt/total household net worth) ⬎ 1, 0 if otherwise Log of total income from all sources in 1996 1 if the household had a cash flow problem in 1996, 0 if otherwise 1 if living on a farm, in a rural area but not on a farm, or in a small town with less than 2500, 0 if otherwise 1 if living in a town of population 2,500–50,000, 0 if otherwise 1 if living in a city of larger than 50,000, 0 if otherwise 噛 people living in the household 1 if one or more members of the household were less than 18 years of age, 0 if otherwise Age in years 1 if female, 0 if male 噛 full time employees Ⳮ (1⁄2 * 噛 part time employees) 1997–the year that business began Age in 1996—age when the manager first started working in the present business 1 1 1 1

if if if if

sole proprietorship, 0 if otherwise partnership, 0 if otherwise subchapter S corporation, 0 if otherwise general corporation, 0 if otherwise


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TABLE 1 (Continued ) Variable Industrial classification Agriculture and mining Construction Manufacturing Transportation Wholesale trade Retail trade Finance, insurance & real estate Professional services

Measurement

1 1 1 1 1 1 1

if agriculture and mining, 0 if otherwise if construction, 0 if otherwise if manufacturing, 0 if otherwise if transportation, 0 if otherwise if wholesale trade, 0 if otherwise if retail trade, 0 if otherwise if finance, insurance, & real estate, 0 if otherwise 1 if professional services, 0 if otherwise

procedure used for assets, liabilities, equity, and income; logistic regression was the procedure used for high debt-to-equity ratio and cash flow problems. The final analytical strategy employed regression analysis to assess the relationship between a specified business financial indicator and its counterpart in the household, holding constant other relevant business and household characteristics. The functional form of the model used to assess this relationship for total business and household assets follows: Log business assets ⳱ f(log household assets, number of employees, business age, business experience, legal organization, industrial classification, location, household size, children in the household, household manager’s age, household manager’s gender). Similar models were established for each of the business financial indicators. Ordinary least squares (OLS) regression was used for the total assets, total liabilities, total equity, and profits models. Logistic regression was the procedure used for high debt and cash flow problems models. Three sets of regressions were run: the first included the whole sub-sample of 307 businesses classified as borrowers and the other two divided the group into women-owned businesses and men-owned businesses (Table 4). In the regressions, the following variables were continuous: number of employees, business age, business experience, household size, and age of the household manager. The following variables were entered as dummy variables: legal organization, industry, location, the presence of children in the household (or not), and the household manager being female (or not). The omitted categories were general corporation, professional services, and urban residence.

Findings Table 2 includes an analysis of the business and household characteristics by gender, including a statistical test of whether each category of variable differs by gender. More than 20% of women-owned businesses in contrast to 11% of men-owned businesses have no paid employees. More than one-half of women-owned firms are sole proprietorships compared to about one-third of men-owned firms. A larger

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TABLE 2 Selected Business and Household Characteristics of Men- and Women-Owned Business Borrowers

Variables Business Characteristics Number of employees None 1 to 4 5 to 9 10 to 19 20 to 99 100 or more Business age 1 year or less 2–5 years 6–10 years 11–20 years 21 or more years Experience of business manager 1 year or less 2–5 years 6–10 years 11–20 years 21 or more years Legal organization Sole proprietor Partnership Subchapter S corporation General corporation Industry classification Agriculture and mining Construction Manufacturing Transportation Wholesale trade Retail trade Finance, insurance, & real estate Professional services Household Characteristics Residence Rural Small town Urban Household size 2 people 3 or 4 people 5 or more people Presence of children in the household (yes)

Men-owned (N ⳱ 242) %

Women-owned (N ⳱ 65) %

11.2 52.1 19.5 6.6 8.7 1.8

21.4* 58.5 12.2 0.3* 5.7 1.9

1.9 21.8 15.9 34.5 25.9

3.7 27.9 21.4 26.6 20.3

2.6 22.5 18.1 40.5 16.3

0.7 30.8 27.6 30.4 10.5

33.8 14.4 29.5 22.3

56.7* 8.3 13.5* 21.5

11.5 10.4 4.2 26.4 24.4 0.8 5.6 16.6

3.3* 12.8 9.5 38.8* 11.5* 10.6* 6.7 6.8*

35.6 38.2 26.2

40.2 25.2* 34.5

33.0 44.0 23.1 54.1

37.9 45.5 16.6 39.9*


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TABLE 2 (Continued )

Variables Age of household manager 30 or less 31–40 41–50 51–60 61 or more Gender of household manager (female)

Men-owned (N ⳱ 242) %

Women-owned (N ⳱ 65) %

9.0 37.6 38.4 12.3 2.6 84.7

2.2 24.5* 46.0 21.9* 5.4 92.6

Note: * significant at .05 or better.

proportion of women than men are in the transportation and retail trade industries. A greater percentage of men than women operate businesses in the agriculture/mining, wholesale trade, and professional service industries. Thirty-eight percent of the men, in contrast to twenty-five percent of the women, lived in small towns. Men were more likely than women to have a child in the household. A greater proportion of women than men were in the older age categories (i.e., 51–60). Results of the bivariate analysis shown in Table 3 indicate that, on average, women-owned businesses have lower levels of total business assets, liabilities, equity, and income than men-owned businesses. Additionally, households occupied by women business owners have lower household incomes than households occupied by men business owners. Table 4 omits the regression coefficients for all business and household characteristics except those related to the household financial indicators, since these are the focus of this study. It shows the six household financial indicator coefficients resulting from the multivariate regressions for three groups: the total sample of 307 ownerborrowers, the 67 women borrowers, and the 242 men borrowers. Five household financial indicators are significant in the men’s equations but not in the women’s. Specifically, total household assets, total equity, high household debt, total household income, and household cash flow problems are predictors of their financial counterparts in the family business for men business owners but not for women business owners.

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TABLE 3 Financial Characteristics of Men- and Women-Owned Business Borrowers

Financial characteristics Total assets (log) Business Household Total liabilities (log) Business Household Total equity (log) Business Household High debt (Debt-to-equity ratio ⬎1) Business Household Total income (log) Business (profits) Household (income) Cash flow problems (yes) Business Household

Men-owned (N ⳱ 242)

Women-owned (N ⳱ 65)

11.82 12.26

10.76* 12.20

10.88 11.02

10.09* 11.20

9.78 10.74

8.42* 11.10

0.37 0.37

0.47 0.37

8.74 11.09

7.35* 10.89*

0.35 0.30

0.28 0.40

Note: * significant at .05 or better. The coefficients for total assets, total liabilities, total equity, and total income are from ordinary least squares (OLS) regression results but the coefficients for high debt and cash flow problems are from logistic regression results.

Conclusions and Implications This study suggests that household financial structure information is a reliable indicator for predicting business financial structure for men-owned small businesses. Lenders depend upon reliable signals from a wide array of financial information, including income and net worth statements from the household. If this information is not a reliable indicator of the lender’s financial expectations for the business, then the business owner may face credit rationing and other barriers to financial credit access. This conclusion seems to be especially important for women entrepreneurs entering the financial market for the first time. Household financial data does not seem to be very useful to lenders assessing women-owned businesses. Hence, they are faced with the task of producing additional financial information to convince the lender that they can be successful business owners. The businesses owned by women in this study are smaller, more


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TABLE 4 The Relationship Between Household and Business Financial Indicators for Men- and Women-Owned Businesses Household financial indicators

Business financial indicators

Total assets (log)

Total liabilities (log)

Total equity (log)

High debt (yes)

Total household income (log)

Cash flow problems (yes)

All businesses (N ⳱ 307) Total assets (log) Total liabilities (log) Total equity (log) High debt (yes) Business profits (log) Cash flow problems (yes)

0.56** 0.22** 0.13 0.68* 1.22** 1.74** Men-owned businesses only (N ⳱ 242)

Total assets (log) Total liabilities (log) Total equity (log) High debt (yes) Business profits (log) Cash flow problems (yes)

0.67** 0.21* 0.19* 1.45* 1.43** 1.05** Women-owned businesses only (N ⳱ 65)

Total assets (log) Total liabilities (log) Total equity (log) High debt (yes) Business profits (log) Cash flow problems (yes)

0.13 0.55*

ⳮ0.3

ⳮ0.55 0.6 19.6

Note: * significant at .05. ** significant at .01. Ordinary least squares (OLS) regressions were used for total assets, total liabilities, total equity, and business profit models, and logistic regressions were used for the high debt and cash flow problems models. The estimated coefficients of other business and household characteristics are not presented in Table 4.

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likely to be sole proprietorships, and more likely to involve transportation and retail trade than businesses owned by men. While these businesses are likely to utilize fewer financial resources because they tend to be smaller, these women business owners are engaged in industries, which require substantial financial resources for fixed assets and inventory. Women borrowers are more likely to use noninstitutional lenders, such as family and friends, to finance their businesses. Hence, women business owners’ financial structure may depend upon types of financial relationships, which are different from those used by men business owners. The household assets, liability, and equity will be much less important to noninstitutional lenders (i.e., family and friends) than institutional lenders. In an environment dominated by institutional lenders, where the five Cs of credit (i.e., character, capacity, collateral, conditions, and capital) are being replaced by loan scoring, women business owners may benefit from a more complete and objective analysis of business ventures. Clearly, casual examinations of household financial statements provide minimal information to the lender concerning the financial structure of women-owned businesses. This information problem requires women business owners to generate additional personal and business financial information to supplement standard household financial income and net worth statements. More complete and objective credit analysis is performed using loan-scoring algorithms. However, women business owners must insist that these algorithms recognize the different financial characteristics of small businesses owned by women. Further research is needed to identify key financial characteristics of households and businesses which differentiate womenand men-owned businesses. These key financial characteristics can be incorporated into loan-scoring models and minimize the degree of information asymmetry between women business owners and their lenders.

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