An analysis across racial lines

Determinants of economic success for women entrepreneurs: an analysis across racial lines

Andrea E. Smith-Hunter


The topic of economic success is central in both the scholarly and real-world discussions of women as entrepreneurs, yet few studies have examined determinants of economic success for women entrepreneurs across differing racial lines. This paper seeks to address the oversight by analyzing data from a nationally recognized database system–Dun and Bradstreet–to determine the role that race and other factors play in impacting the economic success of women entrepreneurs.

Our analysis focuses specifically on the relationship between sales volume (the dependent variable) and number of employees, industry type, race, geographic location and number of years (five independent variables). We suspect, based on previous research, that there will be a relationship between sales volume and. the number of employees and the number of years in business and the type of industry will each be positive. We also posit, based on the current literature, that while these independent variables will explain some of the dependent variable, they will not be the only determinants.


The literature on women entrepreneurship is multidisciplinary and thus is characterized by diverse theoretical perspectives and empirical findings (e.g., Catalyst Guide, 1998; Christopher, 1998; DeLollis, 1997; Goldenberg and Kline, 1999; Renzulli, Aldrich and Moody, 2000). The primary thrust of much of the recent work on women entrepreneurship has focused on what affects their economic success (Moore and Buttner, 1997; Smith-Hunter, 2003; Inman, 2000). These studies have substantially added to the knowledge about women business enterprises among those segments of the population that historically have been understudied in the entrepreneurial sector. Namely, women entrepreneurs and in the case of Inman (2000) and Smith-Hunter, (2003), women entrepreneurs across racial lines. However, all have failed to assess economic success of women business owners across racial lines, using significant sample sizes for each racial stratum.

This paper seeks to answer a number of questions about the success of women business owners using a total sample size of 1,896 women, representing nine states across the United States. It takes a modest, but important step towards assessing the factors leading to economic success for women business owners. Specifically, the paper will analyze factors impacting the economic success of women across racial lines using the statistical tool of regression analysis. The factors to be looked at include: number of years in business, number of employees, geographic location, race and type of industry. The level of sales volume defines economic success in this study. We draw on previous literature of women business owners, women small business owners and women home-based business owners to frame our analyses.


The explosion of women entering the field of business ownership in the last two decades has produced a parallel effect on the women entrepreneurship literature. Some studies have gone the logical route, providing comparative analyses of men versus women (Fabowale, Orser and Riding, 1995; Cromie, 1987; Watkins and Watkins, 1986; Humphreys and McClung, 1981). Others have focused exclusively on women, attempting to flush out the various dimensions and provide us with a richer picture of the woman entrepreneur (Moore and Buttner, 1997; Bowen and Hisrich, 1986; Cromie and Hayes, 1988; Devine, 1994a; Devine, 1994b). While a few have delved into the area of women entrepreneurs who locate their businesses in the home (Priestnitz, 1989; Jurik, 1998; Waldrop, 1994; Heck, Winter and Stafford, 1992; Furry, 1992).

Other studies of women’s entrepreneurship focus exclusively on women, analyzing, among other things, the reasons why women leave the mainstream labor market to pursue business ownership (Moore and Buttner, 1997; Tang, 1995; Shabbir and Di Gregorio, 1996). Most of these are studies of white women; relatively few directly compare white and minority women, and those that do are frequently constrained by data limitations. DeCarlo and Lyons (1979) were among the first researchers to comparatively analyze white and minority women business owners. Examining such characteristics as age, education, entrepreneurial experiences, and marital status, they found that minority women entrepreneurs tended to be more financially disadvantaged than their white peers. The minority women, on the average, were older; less educated, started their businesses at a later age, and were less likely to be married (DeCarlo and Lyons, 1979). However, these women also had a greater number of entrepreneurial experiences prior to starting their businesses, a finding that implies that they had a high level of persistence in the face of their relative disadvantages.

More recently, Inman (2000) compared white and minority women who owned service-oriented businesses, such as hair salons, travel agencies, and law firms. Inman (2000) discovered that minority women business owners were more likely to have been motivated to start their businesses by their more limited options in the mainstream labor market. Furthermore, she found that when minority women started their business, they had fewer financial options and greater difficulty in obtaining the resources needed to pursue their entrepreneurial goals than did their white counterparts (Inman, 2000).

Overall, the literature on women’s business ownership suggests that, in terms of both entrepreneurial options (e.g., occupational choices) and entrepreneurial resources (e.g., sources of capital), women are more disadvantaged than men, and minority women are more disadvantaged than white women. These findings are, of course, well known. However, they are rarely scrutinized with a cross-comparative focus across racial lines for women only, in part because the focus on more obvious areas of research. Moreover, relatively few studies have examined racial differences in women’s business ownership by investigating, for example, possible differences in the geographic location of white and minority women business owners and the impact of this on economic success.

A few of the studies to look specifically at economic success among women business owners were undertaken by Loscocco and Leicht (1993) and Loscocco et al (1991). Loscocco and Leicht (1993) conducted a telephone survey of men and women who owned health service businesses, eating and drinking establishments, and computer sales and software companies in 12 Indiana counties. The study looked at economic success factors such as gross receipts of the business, as well as owners’ earnings received from the business. The results showed gender similarity in the processes through which earnings were determined, although there were differences in many of the predictor variables (Loscocco and Leicht, 1993). In addition, while there were differences in female versus male business owners, the gender discrepancies in sales volume and earnings among the business owners was not seen as particularly wide (Loscocco and Leicht, 1993).

The study by Loscocco et al (1991) looked at the financial success of female and male small business owners. The data came from a pilot study of small businesses in the New England area, with information collected from mailed questionnaires. The authors concluded that the relatively small size of women owned businesses was the major factor explaining their financial disadvantage, when compared to their male counterparts (Loscocco et al, 1991). In addition, the authors concluded that the lack of experience and their concentration in less profitable industries also contributed to the women’s unfavorable financial position (Loscocco et al, 1991).

Another area of interest when analyzing the economic returns for women entrepreneurs is that of home based businesses. The general consensus is that women in home-based businesses tend to earn lower economic returns when compared to other groups (Becker and Moen, 1999; Edwards and Field-Hendrey, 1996; Furry, 1992). This phenomenon can partly be explained by three key reasons. The first reason is the types of industries in which women tend to be involved. Women home-based owners are more likely to operate businesses that are seen as a hobby or an extension of their gendered roles as homemakers compared to their counterparts who locate outside of the home. Such gendered types of businesses have been referred to as “pink collar” businesses by some authors (Ehlers and Main, 1998). Such industries often offer lower returns for their participants (Loscocco and Robinson, 1991; Moore and Buttner, 1997; Smith-Hunter, 2003). The second reason is the lowered number of hours women involved in home based businesses tend to work because of their other commitments (Priestnitz, 1989; Olson, 1997; Edwards and Field-Hendrey, 1996). A third reason that can been advanced for the lowered earnings is the smaller amount of initial capital that women home based business owners have to start a business (Priestnitz, 1989). This latter reason has been used to explain why women are more likely to locate a business in the home in the first place (Priestnitz, 1989).

The thesis of this paper is that the factors that impact the economic success for women entrepreneurs will be different if race is held constant, versus if race is used as an independent variable. Accordingly, our goal is to apply regression analysis to a data set of 1,896 women entrepreneurs composed of varying amounts of diverging racial components. In addition, we have assessed the relationships between race and the following factors: sales volume, geographic location, number of years in business, types of business and number of employees. Alternatively, we have also looked at the relationship between sales volume and the following factors: race, geographic location, number of years in business, types of business and number of employees.


What is the impact of race on women business owners’ success? More specifically, what is the relationship between race and the following: sales volume, geographic location, number of years in business, types of business and number of employees? Alternatively, what is the relationship between sales volume and the following factors: race, geographic location, number of years in business, types of business and number of employees? As a final proposition, what impact does number of years, race, geographic location, types of business and number of employees have on the economic success of a business? What then is the difference in impact if race is held constant?

An answer to these questions can only be garnered by comparing women business owners across racial lines using an adequate sample size in each racial stratum. We aim to answer the previously posed questions as well as a few more largely unanswered questions about women business owners. By focusing on women only, we can get a better sense of the factors impacting the economic success of women entrepreneurs, than is possible in comparisons of women versus men. Having eliminated gender as a key source of variation, we can look more carefully at the dynamics which impact economic success for women across racial lines. Our comparisons of six groups of women are also an important reminder that there is tremendous variation among women entrepreneurs and that there is much to be learned from comparing groups of women, one to another.


One of the most reputable database firms in the United States, Dun and Bradstreet obtains information from millions of public and private businesses–many of which volunteer to be surveyed–as well as from trade tapes, trade associations, court records, government documents, inter-business publications, banks and other financial institutions. In the present study, Dun and Bradstreet (2003) data were used to build a sample frame that was stratified by geographic region, gender, industry type (using the Standard Industry Code), sales volume, number of employees, number of years in business and race. The enterprises included in this frame were located mainly in those cities with the 10 largest populations of women-owned businesses, based on the U.S. Census of 2000, namely: New York, NY; Los Angeles, CA; Chicago, IL; Houston, TX; San Diego, CA; Dallas, TX; San Francisco, CA; Phoenix, AZ; San Antonio, TX and Seattle, WA. In building the sample frame, no restrictions were placed on annual financial figures or number of employees. However, to be included, the businesses had to have been in existence for at least a year. A total of 1,896 women owned businesses were sampled with a racial breakdown as follows: 600 white women, 369 African American women, 394 Hispanic/Latina women, 384 Asian women, 66 Native American women and 83 Indian women.


The following sections provide the results of the statistical analyses. They are presented in separate sections to aid simplicity and to allow the reader to absorb the findings from differing perspectives before being consolidated into a comprehensive focus in the discussion section.

5.1 Descriptive Statistics

Overall, the women entrepreneurs, regardless of their race, have been in business for approximately 1213 years (see table 1). What is surprising is the average sales volume per business results which indicate that the Native American women have the highest average sales volume per business ($2,224,701), followed by Asians ($1,374,561), Indians ($1,022,980), Blacks ($737,679), Hispanics ($669,951) and then Whites ($380,491). In terms of the average number of employees per business, again the Native American women show the highest figures (27), followed by Blacks (11), Asians and Indians (9 each), Hispanics (7) and then Whites (4). The average sales per employee shows Asian women entrepreneurs with the highest figures, followed by Indians, Hispanics, Native Americans and Blacks.

Table 2 shows the type of industry by numbers and percentages and that each racial strata is involved in. As expected, most of the women, regardless of their race, belong to the Services (50.0%) and the retail trade (22.04%) industry, followed by: wholesale trade (8.01%), finance-insurance-real estate (5.11%), manufacturing, transportation-communications-public utilities (4.21%), construction (4.16%) and ending with agriculture-forestry-fishing (1.37%).

As indicated earlier, the women entrepreneurs were sampled from twelve major cities in nine States across the United States. Most of the women sampled were taken from California (33.3%), followed by Texas (14.3%), New York (13.39%), Georgia (7.7%), Michigan (6.8%), Florida (6.17%), Washington, (4.43%) and Arizona (2.95%) see Table 3. With the exception of the African American women in the data, all the other racial strata of women had most of their sampling drawn from California. For the African Americans, the majority of the racial strata came form Texas. The States with the least sampling of women entrepreneurs are as follows for each group: Asians (Florida–1.3%); Blacks (Arizona–1.62%); Whites (Florida–1.83%); Native Americans (Florida–1.51%); Indians (Washington–0%) and Hispanics (Michigan and Arizona–2.53% each).

5.2 Chi-Square Tests

Table 4 represents the chi-square values for the relationship between race and other variables. There was a significant (p=0.00) relationship found between race and the following: geographic location, type of business, number of employees and sales volume. The only relationship that was found not to be significant was race and number of years in business. Alternatively, Table 5 indicates that there were significant results (p = 0.00) found between sales volume and the following: geographic location, years in business, type of business and number of employees.


The first regression analysis used sales volume as the dependent variable and number of employees, years in business, type of business and race as independent variables. It should be noted that the default variable for the race category is “white” and the default variable for the type of industry category is “services”. The results are presented in Table 6 and indicate that the independent variables predict approximately 40% of the dependent variable. This can be restated to mean that the R-square value for the dependent variable is only being accounted for by 40% of the independent variables. Table 7 again looks at another regression analysis and again sales volume is the dependent variable. However, in this instance, the race variable is held constant and the number of years in business, number of employees and type of business are being used as independent variables. With race held constant, the R-square value drops to 39.6%, indicating that the independent variables can now explain 39.6% of the dependent variable. It also indicates that by holding race constant, there is a 0.4% decrease in the R-square value.


To get a better understanding of the determinants of women’s entrepreneurial success, we compared women from various racial strata. Our analysis shows that there is indeed a relationship between race and various variables that have been previously shown as having an impact on women’s entrepreneurial success. We first looked at a number of miscellaneous descriptive statistics on women entrepreneurs. Overall, all the women entrepreneurs, regardless of race, had been in business for approximately the same number of years (12-13 years). Such results are consistent with previous findings from Smith-Hunter (2003) and Inman (2000). In terms of sales and number of employees, the results are surprising. Women entrepreneurs from racial minority groups had higher sales volume and larger number of employees when compared to their white counterparts. These results are in sharp contrast to previous results (Smith-Hunter, 2003; Inman, 2000; Devine, 1994b; DeCarlo and Lyons, 1979) and might be attributed to the sampling frame. More specifically, the Native Americans in the sample were shown to have higher sales volume per business followed by the Asian American women entrepreneurs. For the Native Americans, the larger dollar values could also be attributed to their concentration in industries such as construction and manufacturing and lower concentration in the services industry when compared to their counterparts. For the Asian American women entrepreneurs, they have higher concentration levels in wholesale trade and less concentration in the services industry. Industries such as wholesale trade, manufacturing and construction are shown to offer higher returns (Fratoe, 1988; Loscocco and Smith-Hunter, 2004) when compared to an industry such as services, which offer lower returns on average (Loscocco and Robinson, 1991; Smith-Hunter, 2003).

One of our key questions was whether there was a relationship between race and the following: geographic location, years in business, type of business, number of employees and sales volume. The results indicate that there is in fact a significant relationship between race and geographic location. Results that reconfirms previous findings by Christopher (1998), Light and Rosenstein (1995) and Smith-Hunter (2003). The results also indicate a relationship between race and type of business, supporting results from (Feagin and Imani, 1994; Fratoe 1986; Fratoe, 1988). The percentage distribution of the sampling also shows that all women, regardless of race, are concentrated in the personal services and retail trade industries. Findings that have been echoed repeatedly by Moore and Buttner (1997), Smith-Hunter (2003), Devine (1994b) and Priestnitz (1989). However, these findings are in contrast to results from Loscocco and Smith-Hunter (2004), who recently looked at women home-based business owners and found them concentrated in industries such as manufacturing and business services (Loscocco and Smith-Hunter, 2004).

There were also significant relationships found between race and number of employees. However, the results were not what was expected, with women entrepreneurs from minority groups having larger number of employees when compared to their white counterparts. In the case of Smith-Hunter (2003) and Inman (2000), both authors found that minority women entrepreneurs had smaller number of employees when compared to their white counterparts. It should be noted however, that the authors did not differentiate the minority women into various racial strata, an endeavor that was accomplished with significant sizes of minority women entrepreneurs from five minority racial strata.

The concept of analyzing the relationship between two variables was repeated for sales volume and the following: geographic location, years in business, type of business, number of employees and race. All of the paired relationships were found to have a statistically significant relationship. Findings that been previously documented by Tang (1995), Moore and Buttner (1997), Light and Rosenstein (1995), Smith-Hunter (2003) and Loscocco and Smith-Hunter (2004).

A look at the regression analysis results held some level of disappointment. Using sales volume as a dependent variable and applying all or some of the four independent variables (namely race, number of years in business, number of employees and type of business) yielded a 39-40% predictive explanation. While this is a low R-square on average, one must consider that we were limited in the predictive capacity of economic success because of the low number of independent variables included in the analysis. This would indicate that other variables, not applied to this regression, would help to explain women’s entrepreneurial success. Other factors that have been shown to impact the success of women entrepreneurs include, but are not limited to: educational levels, skills level, pre-business ownership organizational experience, management/supervisory experience, an adequate network structure as well as a continuous access to financial capital (Smith-Hunter, 2003; Moore and Buttner, 1997; Olson 1997; Priestnitz, 1989).

The overall picture that emerges from this research paper suggests that differences across racial lines do play a role in women’s entrepreneurial success. It also reconfirms that there is a relationship between race and other important business ownership variables. However, this distinction in women business owners’ success across racial lines is tempered by the limitations in variables that were analyzed in this particular study. The paper remains significant as a study, which analyzes women entrepreneurs across racial lines, using significant number of minority women in the various racial strata.






Asian 384 (19.20%) 1,374,561 9

Black 369 (18.45%) 737,679 11

White 600 (30.00%) 380,491 4

Hispanic 394 (19.70%) 669,951 7

Indian 83 (4.15%) 1,022,980 9

Native 66 (3.30%) 2,224,701 27


TOTAL 1,896 807.651 8





Asian 152,069 12.77

Black 69,263 12.82

White 87,941 12.74

Hispanic 100,289 12.58

Indian 118,091 12.51

Native 82,212 13.64


TOTAL 98,965 12.90




Agriculture, 26 5 2 15

Forestry, Fishing (1.37%) (1.3%) (0.05%) (2.50%)

Construction 79 8 17 28

(4.16%) (2.08%) (4.60%) (4.67%)

Manufacturing 96 27 10 30

(5.06%) (7.03%) (2.71%) (5.0%)

Transportation 80 14 23 21

Communications (4.21%) (3.64%) (6.23%) (3.50%)

& Public Utilities

Wholesale 152 54 23 41

Trade (8.01%) (14.06%) (6.23%) (6.83%)

Retail Trade 418 117 40 138

(22.04%) (30.47%) (10.84%) (23.0%)

Finance, 97 17 19 38

Insurance and (5.11%) (4.42%) (5.15%) (6.33%)

Real Estate

Services 948 142 235 289

(50.00%) (36.97%) (63.68%) (48.16%)

Total 1896 384 369 600



Agriculture, 3 0 1

Forestry, Fishing (0.07%) (0%) (1.51%)

Construction 16 3 7

(4.06%) (3.61%) (10.60%)

Manufacturing 19 3 7

(4.82%) (3.61%) (10.60%)

Transportation 15 2 5

Communications (3.80%) (2.40%) (7.57%)

& Public Utilities

Wholesale 21 9 4

Trade (5.32%) (10.84%) (6.06%)

Retail Trade 94 16 13

(23.86%) (19.27%) (19.69%)

Finance, 17 5 1

Insurance and (4.31%) (6.02%) (1.51%)

Real Estate

Services 209 45 28

(53.04%) (54.21%) (42.42%)

Total 394 83 66




Arizona 56 8 6 25

(2.95%) (2.08%) (1.62%) (4.16%)

California 632 203 68 177

(33.30%) (52.86%) (18.42%) (29.50%)

Florida 117 5 21 11

(6.17%) (1.30%) (5.69%) (1.83%)

Georgia 146 12 61 48

(7.70%) (3.12%) (16.53%) (8.0%)

Illinois 210 31 49 77

(11.07%) (8.07%) (13.27%) (12.83%)

Michigan 129 13 42 49

(6.8%) (3.38%) (11.38%) (8.16%)

New York 254 62 44 80

(13.39%) (16.14%) (11.92%) (13.33%)

Texas 268 33 69 92

(14.13%) (8.59%) (18.69%) (15.33%)

Washington 84 17 9 41

(4.43%) (4.42%) (2.43%) (6.83%)

TOTAL 1896 384 369 600



Arizona 10 1 6

(2.53%) (1.20%) (9.09%)

California 136 24 24

(34.51%) (28.91%) (36.36%)

Florida 77 2 1

(19.54%) (2.40%) (1.51%)

Georgia 12 8 5

(3.04%) (9.63%) (7.57%)

Illinois 36 12 5

(9.13%) (14.45%) (7.57%)

Michigan 10 10 5

(2.53%) (12.04%) (7.57%)

New York 49 15 4

(12.43%) (18.07%) (6.06%)

Texas 50 11 13

(12.69%) (13.25%) (19.69%)

Washington 14 0 3

(3.55%) (0%) (4.54%)

TOTAL 394 83 66



Race and Geographic Location 380.53 0.00

Race and Years in Business 38.0899 0.1475

Race and Type of Business 218.97 0.00

Race and Number of Employees 124.83 0.00

Race and Sales Volume 113.12 0.00



Sales Volume Geographic Location 109.04 0.00

Sales Volume and Years in Business 107.06 0.00

Sales Volume and Type of Business 392.70 0.00

Sales Volume and Number of 2174.21 0.00


Sales Volume and Race 113.12 0.00





Intercept -89514.25 0.679

Number of Employees 83506.65 7.6E

Years in Business -7029.54 0.465

Asian 472638.25 0.060

Black -195676.82 0.443

Hispanic 129672.39 0.601

Indian 210388.28 0.637

Native American -87479.59 0.860

Agriculture, Forestry, Fishing -84853.33 0.910

Construction 655797.89 0.142

Manufacturing 198091.59 0.628

Manufacturing, Transportation, 1093437.24 0.014

Communications, Public Utilities

Wholesale Trade 2066448.90 7.93E

Retail Trade -65540.42 0.774

Finance, Insurance, Real Estate -32327.56 0.936

R-Square = 0.4009

N = 1896





Intercept -32808.42 0.839

Number of Employees 82817.14 1.754E

Years in Business -7682.70 0.393

Agriculture, Forestry, Fishing -33122.78 0.963

Construction 605810.91 0.133

Manufacturing 261043.61 0.468

Manufacturing, Transportation, 1064826.32 0.013

Communications, Public Utilities

Wholesale Trade 2107770.44 1.594E

Retail Trade 38989.79 0.857

Finance, Insurance, Real Estate 4092.96 0.991

R-Square = 0.3968

N = 1896


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Author Profile

Dr. Andrea Smith-Hunter has been teaching at Siena since 1999, her research focuses on women entrepreneurs across racial lines.

Professor William Engelhardt is an assistant professor of Quantitative Business Analysis at Siena College.

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