Explaining State-Level Differences in WomenOwned Business Performance 

By 

StratEdge 

For 

National Women’s Business Council 

409 Third Street, SW, Suite 210 

Washington, DC 20024 

Under contract number SBAHQ-05-M-0498 

March 13, 2006 

This research study was prepared for the National Women’s Business Council (NWBC; 409 Third Street, 

SW, Suite 210; Washington, DC 20024; Tel. 202 205 3850; Fax. 202 205 6825; www.nwbc.gov; 

info@nwbc.gov) by StratEdge (info@strat-edge.com), a small, woman-owned research and consulting 

firm focusing on economic analysis of competitiveness, innovation, and other major forces shaping the 

global economy. 

The opinions and recommendations of the authors of this study do not necessarily reflect official policies 

or positions of the National Women’s Business Council, the U.S. Small Business Administration or any 

other agency of the U.S. Government. 

 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

I. Executive Summary 

Background 

The National Women’s Business Council (NWBC) contracted StratEdge to carry out a 

creative, secondary analysis of the U.S. Census Bureau’s special tabulations of 1997-2001 data 

on women-owned businesses’ (WOB) performance. These special tabulations, which track 

changes in the number of firms and the number of employees in WOBs between economic 

census years, show that the performance of WOBs varied significantly across the country—from 

a high survival rate of 74% in New York and Connecticut to a low of 60% in Tennessee. Yet, 

little systematic research has been done to evaluate the influence of factors that underlie these 

state-level differences. This study has sought to help fill this research gap by combining the 

special Census tabulations with other available indicators of the business environment in which 

WOBs operate and compete. 

Data and Approach 

StratEdge collected data on three broad sets of factors thought to influence WOB 

performance: (1) individual owner and firm characteristics (age, education level, etc.), (2) 

industry structure (choice of business activity and firm size), and (3) state business environment 

factors (tax rates, infrastructure, 

business support programs, etc.). 

Using a series of regression 

analyses and correlation 

calculations, StratEdge tested the 

importance of these variables in 

explaining observed differences in 

WOB survival rates and changes in 

employment across the 50 states 

between 1997 and 2001. 

Findings and Policy 

Implications 

This study was exploratory in 

nature and several data gaps were 

encountered. In particular, there are 

very few data sets of business 

environment variables specifically relevant to WOBs. Nevertheless, this analysis was suggestive 

of several interesting findings organized below by the broad category of factors that influence 

WOB performance. 

 Individual owner and firm characteristics—Women business owners’ level of 

education is important to better WOB performance across states. States with higher 

proportions of self-employed women with higher degrees had higher WOB survival 

rates and better employment performance. 

Conceptual Model 

Individual Owner & 

Firm Characteristics 

Public Policies & Support Systems Business Climate 

Overall State 

Women-owned 

Business 

Performance Industry Structure of WomenOwned Businesses 

State Business Environment for Women-Owned Businesses 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

 Industry structure—While the distribution of WOBs among different industries does 

not appear to affect a state’s overall WOB performance, the size of WOBs does. 

Despite previous evidence, the type of business that women entrepreneurs choose 

to enter does not appear to affect state WOB performance. However, states with 

higher concentrations of smaller WOBs had lower survival rates. 

 State business environment—The “knowledge economy orientation” of a state (a 

statistical construct measuring the combined effect of the level of venture capital 

investment, educational level of the workforce and degree of broadband penetration) 

had a positive and statistically significant influence on both WOB survival rates and 

growth. Surprisingly, higher personal tax rates (ratio of personal income tax revenue 

to total personal income) are associated with better WOB performance. This could 

be due to some combination of low income-tax states having higher sales, property 

or other taxes that impede business performance, or could indicate that such states 

lack sufficient government revenues to fund policies supporting WOB startup and 

expansion (e.g., higher education, health and other public services). 

Assembling these factors into a comprehensive model to explain WOB performance was 

complicated by limitations in the data. Specifically, a rigorous statistical assessment is 

constrained by having so many explanatory variables with only 50 observations of state-level 

WOB performance. Nevertheless, our analysis suggests that, when controlling for other factors, 

a state’s knowledge economy orientation is one of the most significant predictors of WOB 

performance. 

Conclusion and Recommendations 

Women entrepreneurs and business leaders contribute significantly to the country’s 

economic growth and dynamism. Enhancing the environment within which WOBs operate 

should be premised on a rigorous, objective identification of which specific factors contribute to 

performance and, hence, can enhance the country’s competitiveness. 

This analysis represents one of the first attempts to evaluate systematically the influence 

of factors that underlie state differences in performance. The results of this exploratory research 

reaffirm some things that were previously suggested. For example, the education level of 

women business owners matters, small WOBs perform more poorly than large firms, and having 

access to an educated workforce, venture capital and technology infrastructure all contribute to 

better business performance. While earlier studies had emphasized the importance of these 

factors to WOB performance without specific reference to regional differences, this analysis 

confirms that state-level discrepancies in WOB performance can be partially explained by these 

issues. These findings support continued efforts to provide women entrepreneurs access to 

education, mentoring, financial and technological resources and to invest in enhancing the 

quality of our nation’s workforce more broadly. 

While data were available on a number of indicators, we encountered significant data 

gaps along the way. Filling in these gaps will enhance the productivity of future research efforts 

to better understand what factors lead to improved performance of WOBs and what 

StratEdge 2 
 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

state/national policy interventions may improve WOB outcomes. Specifically, StratEdge offers 

two key recommendations: 

First, NWBC should direct attention to already administered surveys to expand the 

number of indicators focused on WOBs and to collect this data by state and by industry. For 

example, data on firm-level characteristics such as reliance on venture capital by women 

entrepreneurs may be collected relatively easily using the Money Tree Survey administered by 

the National Venture Capital Association. Similarly, the Federal Reserve Board and Census 

Bureau could be requested to compile and make available more detailed data by state on 

women business owners, including personal wealth (average household income), use of 

financial instruments, participation in government contracting, etc. Furthermore, more detailed 

data sets could be developed over time, and at finer geographic levels (county or metropolitan 

areas), which would permit more rigorous statistical studies. 

Second, to support more effective research by related and unrelated parties, NWBC 

should consider supporting the establishment of a data clearinghouse or repository of research 

data sets. The current research effort has highlighted the lack of readily accessible, detailed 

data sets at the firm, regional, and industry level relevant to the analysis of WOBs. Simply 

collecting such data sets and making them more accessible could catalyze further research on 

these issues, leveraging a broader research community to investigate these important issues. 

StratEdge 3 
 

 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

II. Introduction 

Representing half of the potential pool of entrepreneurs and business leaders, women 

are important contributors to the nation’s economic growth and dynamism. In 2004, there were 

approximately 10.6 million privately held businesses that were at least 50% woman-owned. 

From 1997 to 2004, U.S. women-owned businesses (WOBs) appear to have grown at rates 

exceeding the national average for all privately held firms.1 This has not been the case 

historically and continues not to be the case in many industries and many parts of the country 

where WOBs have been found to be less successful, on average, than those owned by men, 

even when they operate in the same industries.2 

Indeed, the U.S. Census Bureau’s special tabulations, which track changes in the 

number of firms and number of employees in WOBs from 1997 to 2001 by state, show that the 

performance of WOBs varied significantly across the country—from a high survival rate of 74% 

in New York and Connecticut to a low of 60% in Tennessee. It is also observed that 

employment in the average WOB in existence in 1997 had declined by 9.3% by 2001 and that 

the rate of employment contraction or expansion of WOBs varied by state. However, little 

research has been done to evaluate systematically the influence of factors that underlie these 

regional differences. 

StratEdge was contracted by the National Women’s Business Council (NWBC) to carry 

out a creative, secondary analysis of U.S. Census Bureau data on issues pertinent to WOB. We 

proposed to identify factors that may partially explain differences in the state-by-state 

performance of WOBs. The existing literature has generally ascribed gender-based business 

performance discrepancies to three broad sets of factors: (1) the overall business environment 

within which the firm operates; (2) the firm owner’s choice of business activity; and (3) individual 

factors, such as the entrepreneur’s skills, experience, and financial strength. Several studies 

find that female small business owners’ failure to achieve levels of success comparable to those 

of male owners stems from less work experience and the tendency for WOBs to be 

concentrated in the least profitable, highly-competitive, and low-growth industries.3

 Others find 

that women opt to start small businesses because of workplace disadvantages.4

 Some take the 

1 The number of women- and equally-owned firms grew by 17% compared to 9% among all privately held firms, while 

employment in women- and equally-owned firms increased by 24% compared to 12% among all privately held firms. 

Meanwhile, revenues of women- and equally-owned firms rose by 46% compared to 34% among all privately held 

firms. National Women’s Business Council. 2005. “Women Business Owners and Their Enterprises,” Fact Sheet, 

March. 

2 Loscocco, K.A., and J. Robinson. 1991. “Barriers to Small Business Success Among Women,” Gender and Society 

5(4): 511-532. 

3

 Loscocco, K.A., J. Robinson, R.H. Hall, and J.K. Allen. 1991. “Gender and Small Business Success: An Inquiry into 

Women’s Relative Disadvantage.” Social Forces 70 (1): 65-85; and Kalleberg, A.L., and K.T. Leicht. 1991. Small 

Business Success and Survival: Individual and Structural Determinants of Organizational Performance. Academy of 

Management Journal. 4

 England, P., G. Farkas, B. Kilbourne, and T. Dou. 1988. Explaining Occupational Sex Segregation and Wages: 

Findings from a Model with Fixed Effects. American Sociological Review, 53:544-58; and Bender, H. 1980. Report on 

Women Business Owners. American Management Association. 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

view that enhancing public services may be the most effective short-term approach for states 

and regions concerned with small business retention and expansion.5 

However, there has been relatively little systematic research addressing the overall 

business environment as it relates specifically to WOBs—what types of policies, investments, or 

support systems are most advantageous to women entrepreneurs and business leaders. And 

most importantly, how do these factors affect the performance of WOBs? 

III. Data and Methodology 

Based on existing literature, our conceptual model illustrated below relates the overall 

WOB performance in a state to three broad categories of factors: (1) the overall business 

environment within which the firm operates, including public policies, business support systems 

and the overall business climate in the state; (2) individual owner characteristics, such as the 

entrepreneur’s education, experience and personal wealth; and (3) the firm owner’s choice of 

business activity, or the structure of industries in which WOBs operate. 

Conceptual Model 

Individual Owner & 

Firm Characteristics 

Public Policies & Support Systems Business Climate 

Overall State 

Women-owned 

Business 

Performance Industry Structure of WomenOwned Businesses 

State Business Environment for Women-Owned Businesses 

5 Seley, J.E. 1981. Targeting Economic Development: An Examination of the Needs of Small Businesses, Economic 

Geography 57 (1):34-51. 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

We constructed a simple, linear regression model to evaluate the importance and 

influence of these many factors on state WOB performance. Before constructing the final model, 

each group of variables was tested for their individual influences using a series of correlation 

and bivariate regression tests. Only those variables that had statistical significance as individual 

predictors were included in the final step-by-step regression model. The remainder of this 

section discusses each of the variables used in the model and their data sources. (See 

Appendix 1 for more detailed data citations and Appendix 2 for actual data for select variables.) 

Dependent Variables: WOB Performance—The U.S. Census Bureau’s special 

tabulations contain two performance measures of WOB establishments in each state: survival 

rates and employment change between 1997 and 2001. State-level data for these two variables 

collected from the February 2005 NWBC Issue in Brief publication6

 show wide variation across 

states. For example, while the national average business survival rate was 68.5%, Connecticut 

and New York had the highest survival rate of 74.4% and Tennessee had the lowest rate of 

60.5%. Similarly, while employment in WOBs in New Jersey grew at an impressive 18.7% 

between 1997 and 2001, New Mexico witnessed the worst rate of employment contraction 

(23.5%) during the same period. 

Independent Variables—Independent variables were grouped under three primary 

influence categories: individual owner and firm characteristics, industry structure, and state 

business environment for WOBs. 

 Individual Owner and Firm Characteristics—Business owners bring different skills 

and resources to bear on their companies. For example, to the extent that the 

women business owners in some states are better educated and have greater 

financial resources (e.g., wealth, household income, access to credit), we would 

expect the WOBs in those states to perform better. One would like to have 

comprehensive data on the specific characteristics of women business owners in 

each state in order to account for these differences, ideally from the same source or 

a source using data comparable to the Census special tabulations. Unfortunately, the 

Census’s Survey of Business Owners contains only limited firm and business owner 

characteristics data. In lieu of this data, we obtained microdata on women 

respondents who identified themselves as being “self-employed” in the 1997 Current 

Population Survey (CPS) from the Bureau of Labor Statistics (BLS).7

 Two sets of 

data were examined, one relating to the education level of women business owners 

and the second relating to her experience proxied by age. 

Regarding education, we examined two variables: (1) the percentage of selfemployed women that have at least an associate degree and (2) the percentage of 

self-employed women in the state with at least a 4-year college degree. The level of 

education of women entrepreneurs varies significantly across the United States. In 

Indiana, only 13.0% of all self-employed women had a college degree whereas the 

ratio was 40.6% for New Jersey and 69.6% for District of Columbia. 

6

 "Trends in Women-Owned Employer Establishments: 1997-2001" Issue in Brief, NWBC, Feb. 2005, Table 1. 7

 It should be noted that the extract includes some small sample sizes for some of the states, somewhat limiting the 

statistical reliability of this data. 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

Years of business experience was not available. In its place, we use age as a 

proxy—the percentage of self-employed women 45 years or older in the total 

population of self-employed women in the state. Like educational attainment, this 

variable varies widely across states. For example, 60.8% of all self-employed women 

in Alabama were 45 years or older, whereas only 34.0% of Alaskan self-employed 

women were in this age category. We would expect less experienced (younger) 

women business owners to be less successful than their more experienced (older) 

counterparts. 

The study team would have liked to include other variables relating to owner and firm 

characteristics, including household income or wealth (a measure of the financial 

resources an entrepreneur is able to bring to a venture), true measures of business 

experience (such as years in the labor force, or better yet, years as a business 

owner). Unfortunately, we were unable to locate data on these issues in a format 

useful for this analysis. In some instances, existing data sets were identified, such as 

the CPS and the Census Bureau’s Integrated Longitudinal Business Database, both 

of which contain rich data on business owners. However, these resources require 

significant manipulations and/or special access, which put them out of the scope of 

this study. 

 Industry Structure—The business activity (i.e., type of industry) women 

entrepreneurs choose to enter into may also affect state WOB performance. For 

example, if all of a region’s WOB were in the construction industry, we would expect 

the state’s overall WOB performance to reflect the performance of the construction 

industry in that state. To account for this influence, we consider the share of WOBs 

in manufacturing and services because of the higher rate of survival of WOBs in 

manufacturing noted in the literature and the high degree of intensity of WOBs in 

service industries (over 55% of all WOBs in the United States). This data was 

available directly from the Census Bureau’s 1997 Survey of Minority and WomenOwned Business Enterprises, the same foundation source for the special tabulations 

used as indicators of WOB performance (which track how these 1997 firms 

performed between 1997 and 2001). 

Smaller firms are typically more volatile than larger, more established enterprises. To 

isolate the effects of firm size, we constructed and used two variables: (1) the share 

of WOBs with receipts less than $500K and (2) the share of WOBs with receipts less 

than $100K as a percentage of all WOBs in the state. Estimation and modeling of 

these two variables were based on the premise that challenges and opportunities for 

business growth affect firms differently based on their size. Wide variations were 

observed for these two variables across the 50 states, with the portion of WOBs with 

receipts less than $500k ranging from 61.6% in Hawaii to 85.4% in North Dakota. 

 State Business Environment for Women-Owned Businesses—There are a multitude 

of variables that collectively constitute the business environment in which WOBs 

operate. We group those variables into two main influence categories: (1) public 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

policies8 and (2) state business climate. Public policies include such aspects as state 

and local taxes that business owners are subjected to in each state. For this analysis 

we considered two tax-related variables: (1) the state corporate tax rate and (2) the 

state and local personal income tax rate. The corporate tax rate was calculated as 

the ratio of total state corporate tax revenues collected to the total state business 

income. Similarly, the personal tax rate was calculated as total state and local 

personal income tax revenues as a percentage of total state personal income. 

Business climate is a broader term that we use to refer to the overall ease of 

conducting business, particularly for WOBs, in a state. In this analysis, we have 

included: 

−	 Access to financing—(1) small business loan intensity measured as the value 

of small business loans (< $1m in size) per worker, (2) commercial and 

industrial loan intensity measured as total commercial and industrial loan 

values as a percentage of total bank assets for FDIC institutions, and (3) 

venture capital investments measured as total venture capital investment 

dollars in the state per establishment. 

−	 Access to a quality workforce—(1) education level of potential workforce 

measured as the number of persons 25 and older with a college degree or 

higher and (2) women’s participation in the labor force measured as the 

percentage of civilian female population in the civilian labor force. 

−	 Access to technological and physical infrastructure—(1) broadband 

penetration measured as the number of high-speed Internet lines over 

population and (2) highway miles in the state per 1,000 population. 

−	 Small business climate score measuring the degree to which the policy 

environment supports entrepreneurship. This variable is, itself, a composite 

of indicators including tax policies, regulatory costs, workers compensation 

and others called the “business survival index.” It was developed by the 

Small Business and Entrepreneurship Council. 

We encountered particular data gaps related to state business environment factors 

that are specific to WOBs. Available data for many state business environment 

factors did not distinguish between the gender of business owners. While it is true 

that WOBs operate in the same business environment as other firms, there could be 

differences in the way these factors impact WOBs. It would be interesting to analyze 

8

 One additional policy-relevant line of inquiry is to examine the role of state support systems for women business 

owners, such as mentoring programs and other women-focused business extension services within states’ small 

business development offices. Some data are available, such as the number of SBA-sponsored Women’s Business 

Centers, state chapters of the National Association of Women Business Owners (NAWBO), and state chapters of the 

Women Presidents’ Organization (WPO). However, the number of such organizations in each state is, at best, a 

rough indication of the reach of these organizations. A better indicator would be a measure of the degree of 

assistance provided to women entrepreneurs by these organizations and the number of women business owners 

assisted. Preliminary tests were run on the number of organizations and found to have no statistically significant 

influence on WOB performance. However, this indicator was not included in this study because of the aforementioned 

measurement issues. 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

whether there are particular business environment characteristics that are more 

advantageous to WOB performance or more serious impediments to WOB 

performance than for men-owned businesses. One example of this might be the cost 

of healthcare and whether this affects WOB expansion more than men-owned 

businesses. Similarly, data on financial resources was either unavailable at the state 

level (e.g., Survey of Small Business Finance which captures data on women 

business owners’ use of institutional credit) or unavailable by gender (e.g. Money 

Tree Survey of venture capital investment). 

IV. Findings 

This section presents the findings of the two models used in this analysis. Both models 

use the same set of independent variables, but examine their impact on the two different WOB 

performance indicators. The first model predicts the state-level performance of WOB in terms of 

the survival rates of those businesses (i.e., how many WOB in existence in 1997 were still in 

existence in 2001). The second model predicts the performance of WOB in terms of the rate of 

change of employment between 1997 and 2001 by state. 

Explaining Survival Rates of Women-Owned Businesses 

Bivariate regressions relating each independent explanatory variable to state WOB 

survival rates were performed. The table on the following page summarizes the statistical 

significance and regression coefficients of these individual variables. 

The level of education of women entrepreneurs in a state is a key predictor of state-level 

differences in WOB survival rates. The percentage of self-employed women in a state with at 

least an associate degree exhibited a positive and statistically significant impact (B=11.45, 

p=0.015) on WOB survival rates. A similar positive influence was observed when the 

percentage of self-employed women in a state with at least a college degree was used to 

predict the state-level WOB survival rate (B=10.17, p=0.027). (See chart below.) 

Relationship Between the Education of Women 

Business Owners and Firm Survival Rates 

55 

60 

65 

70 

75 

80 

10 20 30 40 50 

Percentage of women business owners in state with college degree or higher 

Survival rate of WOB 1997-2001 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

Explaining Survival Rates of Women-Owned Businesses 

Bivariate Regression Results 

Independent Variable 

Statistical 

Significance 

Unstandardized 

Regression 

Coefficient B 

Individual Owner and Firm Characteristics 

Education (associate and higher) Yes (p=.015) 11.45 

Education (college grad and higher) Yes (p=.027) 10.17 

Experience Not Significant 

Choice of Business Activity and Industry Structure 

Manufacturing Concentration Not Significant 

Services Concentration Not Significant 

Proportion of Small WOB (<$500k) Yes (p=.016) -26.98 

Proportion of Small WOB (<$100k) Yes (p=.004) -24.02 

Public Policies & Business Climate in the State 

Public Policies 

Corporate Taxes Not Significant 

Personal Income Taxes Yes (p=.003) 118.48 

Access to Financing 

Small Business Loan Intensity Not Significant 

Commercial & Industrial Loans Not Significant 

Venture Capital Investments Yes (p=.022) 0.001 

Access to Workforce 

Workforce Education Level Yes (p=.017) 0.24 

Women’s Labor Force Participation Not Significant 

Access to Infrastructure 

Broadband Penetration Yes (p=.001) 61.66 

Transportation Infrastructure Not Significant 

Small Business Climate Score 

Small Business Climate Index Yes (p=.002) 0.161 

With regard to variables representing industry structure, the share of WOBs in 

manufacturing or services in the state did not have statistical significance. However, the 

structure of the state economy with regard to the size of WOBs did have a noticeable effect on 

WOB survival rates. Both of the WOB size variables considered had a significant, negative 

influence on the survival rates of WOBs. In other words, states with larger proportions of small 

WOBs had lower WOB survival rates. 

When modeled as individual predictors of survival rates of WOBs, the personal income 

tax rates to which women business owners are subjected turns out to be statistically significant 

with the unstandardized coefficient of 118.48. In other words, a higher personal tax rate had a 

positive and significant effect on WOB performance (i.e., a higher state personal tax rate is 

associated with better WOB survival rates). On the other hand, corporate tax rates do not show 

any statistically significant influence on the survival rates of WOB across states. 

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Venture capital investment is positively associated with WOB survival rates although the 

degree of influence is marginal. The other two measures of access to financing, namely, the 

small business loan intensity in the state and the commercial and industrial loan availability to 

businesses, do not have any statistical significance in predicting the survival rate of WOB. 

The level of education of the available workforce shows a high degree of positive 

association (B=0.24, p=0.017) with WOB survival rate. However, women’s participation in the 

labor force, signifying the degree of availability of women workers to businesses in the state 

(and consequently to WOB in the state) does not have any significant association with the 

survival rate of WOB. 

With regard to access to infrastructure, broadband penetration has a significant, positive 

effect (B=61.66, p=0.001) on the survival rate. However, transportation infrastructure does not 

have any statistically significant effect on the WOB survival rate. In addition, the small business 

climate score (small business climate index measuring the degree of policy environment support 

for entrepreneurship in the state) revealed a positive influence (B=0.161, p=0.002) on the WOB 

survival rate. 

Those variables that showed statistically significant influences individually on the survival 

rate of WOBs were included in a step-by-step, multivariate regression model. At the first step, it 

was evident that venture capital investment was strongly and positively correlated with the level 

of education of the workforce and broadband penetration, suggesting possibly that a highly 

educated workforce is strongly associated with a robust technological infrastructure and attracts 

higher levels of venture capital. These indicators are the typical indicators of the knowledge 

economy. A factor analysis was performed on these three variables. The test yielded very high 

eigenvalues, suggesting that they are measuring the same core issue. Therefore, we combined 

these three variables into one construct representing the “knowledge economy orientation” of 

the state. The newly constructed factor variable, along with the small business climate index 

and personal tax rates, were modeled in a multivariate regression model. In the combined 

model, of all the policies and business climate related variables, only the knowledge economy 

orientation construct retained statistical significance. (Model results can be found in Appendix 

3.) This model explained 34.2% of the differences in WOB survival rates across the 50 states. 

Subsequent models, incorporating owner and industry characteristics failed basic 

statistical tests for model validity, but were still suggestive that the knowledge economy 

orientation is associated with better WOB performance. 

Explaining Employment Changes of Women-Owned Businesses 

Changes in employment in WOBs capture a different aspect of business performance as 

compared to the survival rate of WOBs. Changes in employment reflect the strategic choices— 

expansion or reduction of workforce—taken by business owners when faced with business 

opportunities and challenges. Those strategies, and the consequent effect on employment 

change, may have different levels of dependence on variables representing the business 

climate and competitive forces in the states. Therefore, in this study, we take a parallel path to 

analyzing the effects of public policies and business climate variables as well as the industry 

structure variables on state-level changes in employment for WOBs between 1997 and 2001. 

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– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

We begin this analysis by conducting individual bivariate regressions on the independent 

variables as in the previous case. The following table summarizes these results. 

Explaining Employment Changes of WOB 

Bivariate Regression Results 

Independent Variable 

Statistical 

Significance 

Unstandardized 

Regression 

Coefficient B 

Individual Owner and Firm Characteristics 

Education (associate and higher) Yes (p=.009) 28.12 

Education (college grad and higher) Yes (p=.022) 24.34 

Experience Yes (p=.016) -43.58 

Industry Structure 

Manufacturing Concentration Not Significant 

Services Concentration Not Significant 

Proportion of Small WOB (<$500k) Not Significant 

Proportion of Small WOB (<$100k) Not Significant 

Public Policies & Business Climate 

Public Policies 

Corporate Taxes Not Significant 

Personal Income Taxes Yes (p=.049) 185.06 

Access to Financing 

Small Business Loan Intensity Not Significant 

Commercial & Industrial Loans Not Significant 

Venture Capital Investments Yes (p=.042) 0.001 

Access to Workforce 

Workforce Education Level Yes (p=.009) 0.61 

Women’s Labor Force Participation Not Significant 

Access to Infrastructure 

Broadband Penetration Yes (p=.001) 136.63 

Transportation Infrastructure Yes (p=.007) -0.39 

Small Business Climate Score 

Small Business Climate Index Not Significant 

The level of education of women entrepreneurs emerges as closely associated to 

observed state-level differences in changes in employment for WOB. (For associate degree or 

higher, B=28.12, p=0.009; for college degree or higher, B=24.34, p=.022.) 

With regard to variables representing industry structure, shares of WOBs in 

manufacturing or services did not have statistical significance on employment changes. 

However, contrary to the analysis of business survival rates which were associated with the size 

of WOBs, the proportion of WOBs that are small does not have a noticeable effect on 

employment performance. 

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When modeled individually, the state and local personal income tax rates to which 

women business owners are subjected again turns out to be positively related to WOB 

performance (B=185.06, p=0.049). As in the analysis of survival rates, corporate tax rates did 

not have any statistically significant influence on change in employment in states. 

Among the three variables representing access to financing, only venture capital 

investment is positively associated with the survival rate of WOBs albeit a marginal degree of 

influence. The other two measures of access to financing, namely, the small business loan 

intensity in the state and commercial and industrial loans do not have any statistical significance 

in predicting WOB changes in employment. 

Similar to the case of survival rates, while employment changes of WOBs appear to be 

strongly associated with the workforce’s overall level of education, they are not associated with 

higher women’s participation in the labor force. 

With regard to access to infrastructure, broadband penetration, has a significant, positive 

effect (B=136.63, p=0.001) on changes in employment. Unlike the case of business survival 

rate, transportation infrastructure does have a statistically significant, though negative effect on 

the change in employment at WOBs (B= -0.386, p=0.007). (Although the model does not 

explain causal relationships, it is intriguing that a more significant transportation infrastructure is 

associated with declining or lower job growth among WOBs. In addition, the small business 

climate score (small business climate index measuring the policy environment support to 

entrepreneurship in the state) does not reveal a statistically significant influence on change in 

employment. 

Those variables that showed a statistically significant influence on employment changes 

in WOBs were included in a step-by-step, multivariate regression model. When treated together, 

the public policy and business climate related variables had a different combined effect on 

change in employment of WOBs than when treated separately. Once again, to address the 

strong correlation among venture capital investments in the states, the level of education of the 

workforce and broadband penetration, a “knowledge economy orientation” construct was used 

in the model. In the combined model, of all the policies and business climate related variables, 

only the knowledge economy orientation construct retains statistical significance (p=0.022). 

Together, this model explained 28.7% of the differences in changes in employment in WOB. 

(Model results can be found in Appendix 4.) 

V. Conclusions and Recommendations 

Women entrepreneurs and business leaders contribute significantly to the country’s 

economic growth. If WOBs are less successful, on average, in some states than in others, there 

may be policy interventions that can be taken to better support them. Such interventions should 

be premised on a rigorous, objective identification of which specific factors are contributing to 

these performance differences. 

This analysis represents one of the first attempts to evaluate systematically the influence 

of factors that underlie regional differences in the performance of WOBs. This analysis, though 

StratEdge 13 
 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

exploratory and limited by data gaps and shortcomings, was nevertheless suggestive of several 

interesting findings: 

 Individual owner and firm characteristics—Women business owners’ level of 

education is important to better WOB performance across states. The percentage of 

self-employed women in a state with higher degrees (associate level and above) 

exhibited a positive and statistically significant impact on both WOB survival rates 

and changes in employment. 

 Industry structure—While the distribution of WOBs among different industries does 

not appear to affect a state’s overall WOB performance, the size of WOBs does. 

Despite previous evidence, the type of business that women entrepreneurs choose 

to enter does not appear to affect state WOB performance. However, states with 

higher concentrations of smaller WOBs had lower survival rates. 

 State business environment —The “knowledge economy orientation” of a state (a 

statistical construct measuring the combined effect of the level of venture capital 

investment, educational level of the workforce and degree of broadband penetration) 

had a positive and statistically significant influence on both WOB survival rates and 

growth. Surprisingly, personal income tax rates are significantly and positively related 

to WOB performance (higher tax rates are related to better WOB performance). This 

finding merits further study, though it is important to note that the effect is 

insignificant in the full regression model. One interpretation is that states with higher 

income taxes have the resources to provide other services and support systems that 

enhance WOB performance, such as greater investments in education and health 

and other public services. Another possibility is that low income-tax states actually 

have higher rates for other taxes (sales, property, etc.) that impede WOB 

performance. Our results do not support causal conclusions and given the significant 

research suggesting that high tax rates either impede or have little impact on 

business performance, it is likely that there is something else behind these results. 

Assembling these factors into a comprehensive model to explain WOB performance was 

complicated by limitations in the data. Specifically, a rigorous statistical assessment is 

constrained in this instance by the necessity of using so many explanatory variables to account 

for the complexity that is business performance, with only 50 observations of state-level WOB 

performance. Nevertheless, our analysis suggests that, when controlling for other factors, a 

state’s knowledge economy orientation is one of the most significant predictors of WOB 

performance. 

In summary, the results of this exploratory research reaffirm some things that were 

previously suggested. For example, the education level of women business owners matters, 

small WOBs perform more poorly than large firms, and having access to an educated 

workforce, venture capital and technology infrastructure all contribute to better business 

performance. While earlier studies had emphasized the importance of these factors to WOB 

performance without specific reference to regional differences, this analysis confirms that statelevel discrepancies in WOB performance can be partially explained by those factors. These 

findings support continued efforts to provide women entrepreneurs access to education, 

StratEdge	 14 
 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

mentoring, financial and technological resources and to invest in enhancing the quality of our 

nation’s workforce more broadly. 

While the results of this exploratory research are illuminating, significant data gaps were 

encountered along the way. Addressing these gaps can enhance the productivity of future 

research efforts to better understand what factors lead to improved performance of WOBs and 

what state/national policy interventions may improve WOB outcomes. Specifically, StratEdge 

offers two key recommendations: 

First, NWBC should direct attention to already administered surveys to expand the 

number of indicators focused on WOBs and to collect this data by state and by industry. For 

example, data on firm-level characteristics such as reliance on venture capital by women 

entrepreneurs may be collected relatively easily using the Money Tree Survey administered by 

the National Venture Capital Association. Similarly, the Federal Reserve Board and Census 

Bureau could be requested to compile and make available more detailed data by state on 

women business owners, including personal wealth (average household income), use of 

financial instruments, participation in government contracting, etc. Furthermore, more detailed 

data sets could be developed over time, or at a finer geographic level (county or metropolitan 

areas), which would permit more rigorous statistical studies. 

Second, to support more effective research by related and unrelated parties, NWBC 

could establish a data clearinghouse, or repository of research data sets. This research has 

highlighted the lack of readily accessible, detailed data sets at the firm, regional, and industry 

level relevant to the analysis of WOBs. By collecting and making such data sets more readily 

available, NWBC could catalyze further research on these issues, leveraging a broader 

research community to investigate these important issues. 

StratEdge 15 
Appendix 1. Data Sources 

Variable Description Time 

Coverage 

IndustryCoverage/Detail 

Source 

PERFORMANCE VARIABLES 1. WOB survival rates 1997-2001 

Survival rate of WOBs in existence in 1997 between 1997 and 2001 

1997-2001 By state, by gender 

and by industry at national level. 

NWBC (2005) "Trends in Women-Owned Employe

in Brief, NWBC, Feb.; Table 1. 

2. WOB employment change 1997-2001 

Percent change in employment for WOBs 

1997-2001 By state, by gender 

and by industry at national level. 

NWBC (2005) "Trends in Women-Owned Employe

in Brief, NWBC, Feb.; Table 1. 

INDIVIDUAL BUSINESS OWNER CHARACTERISTICS Education Percentage of women business 

owners (self-classified in survey as self-employed women in own incorporated or unincorporated business) with at least a college (a) or graduate degree (b) 

1997 By state, by gender Current Population Survey microdata extracted by 

so findings may not be statistically robust 

Experience Percentage of women business 

owners who are 45 years or older in State (see definition above) 

1997 By state, by gender Current Population Survey microdata extracted by 

so findings may not be statistically robust 

Industry Structure of Women-Owned Businesses Industry Concentration – Manufacturing and Services 

Percentage, by number of firms, of WOBs in manufacturing and services industry segments. (calculated) 

1997 By 2-digit SIC code, by 

state, by gender 

Census Bureau, 1997 Survey of Minority and WomTable 3. 

Proportion of WOB that are small 

WOBs with receipts <$500k and with receipts <$100k as % of all WOBs in state. 

1997 By state, by industry for 

country 

StratEdge calculations based on Census, Special Businesses, 2003. 
STATE BUSINESS ENVIRONMENT FOR WOMEN-OWNED BUSINESSES 

Public Policies & Support Systems State Corporate Tax Rate 

Total state corporate tax revenues/total state business income 

2001 By state Census Bureau & Bureau of Economic Analysis: 

http://www.census.gov/govs/www/statetax.html http://www.bea.doc.gov/bea/regional/data.htm 

State Personal Income Tax Rate 

Total personal income tax revenues as % of personal income 

2001 By state Census Bureau, State and Local Gov Finances 

http://www.census.gov/govs/www/estimate.html BEA Annual State Personal Income – www.bea.do

Small Business Loan Intensity 

Value of small business loans (<$1m) per worker in the state 

2000 By state, not by gender SBA, Office of Advocacy; Small Business & MicroB

States (CRA data, Table 4) http://www.sba.gov/ad

Commercial & Industrial Loan Intensity 

C&I loan values as % total bank assets (FDIC institutions) 

2000 By state, not by gender Federal Deposit Insurance Corporation (FDIC), Sta

http://www2.fdic.gov/SDI/SOB/ 

Venture Capital Investments 

Total VC investments per establishment in state ($/establishments) 

2001 By state, not by gender PriceWaterhouse Coopers, Moneytree Survey, 

http://www.pwcmoneytree.com/moneytree/index.jsestablishments per state. 

Workforce Education Level 

Persons 25 years or older with college degree or more. 

1998 By state, by gender Census Bureau; Current Population Reports, P20-

http://www.census.gov/prod/www/statistical-abstra

Women’s Labor Force Participation 

Percent of civilian non-institutional female population in the civilian labor force (percentage) 

1997 By state, by gender Census Bureau, 1999, Statistical Abstract of the U

http://www.census.gov/prod/www/statistical-abstra

Broadband Penetration 

# of high-speed lines per 1,000 people 2003 By state, not by gender Federal Communications Commission (FCC), http

Transportation Infrastructure 

Highway miles per 1,000 people 2002 By state, not by gender Department of Transportation, Highway Statistics 20

www.fhwa.dot.gov/ohim/ohimstat.htm. 

Small Business Climate 

Small business survival index for “policy environment in support of entrepreneurship” 

By state, not by gender Small Business and Entrepreneurship Council, Sm

http://www.sbsc.org 
 

 

 

 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

Appendix 2. Data Table of Statistically Significant 

Variables Affecting WOB Performance 

State WOB Performance Business Owner 

Characteristics Firm Characteristics Business Environment Factors 

Survival 

Rate 

Employ. 

Change (%) 

% of selfemploy. 

women with 

college 

degree 

% of selfemploy. 

women age 

45+ 

% of WOB 

with < 

$500K 

receipts 

% of WOB 

with < 

$100K 

receipts 

State and 

local pers. 

tax rate 

Total VC $ 

invest. per 

estab. 

Educ. level 

of 

workforce 

(% of 25+ 

pop. with 

college 

degree) 

Broadband 

Penetration 

United States 68.5 -9.3 28.3 47.9 73.6 31.0 2.3 $5,448 24.4 0.10 

Alabama 64.8 -11.4 16.2 60.8 75.7 32.3 1.9 $982 20.6 0.08 

Alaska 64.2 -13.9 30.5 34.0 76.1 28.6 0 $5,700 24.2 0.11 

Arizona 62.2 -6.5 24.8 38.0 77.0 31.6 1.5 $1,561 21.9 0.10 

Arkansas 61.3 -11.9 16.6 56.3 74.1 36.5 2.5 $150 16.2 0.06 

California 71.2 -14.5 34.9 45.8 67.9 22.1 2.9 $15,585 26.4 0.12 

Colorado 65.8 -0.4 36.2 46.2 78.0 37.0 2.3 $8,778 34.0 0.09 

Connecticut 74.4 -7.1 40.4 35.3 72.2 27.4 2.5 $5,551 31.4 0.13 

Delaware 64.3 -4.4 35.0 53.8 75.3 37.7 3.0 $6,543 25.1 0.08 

District of Columbia 66.7 -18.1 69.6 55.5 63.1 20.0 0 n/a 36.5 n/a 

Florida 65.1 -13.7 33.4 47.0 76.1 30.0 0 $2,007 22.5 0.12 

Georgia 65.5 -10.1 27.5 40.0 74.1 31.3 2.7 $4,194 20.7 0.11 

Hawaii 73.2 -16.1 27.4 53.6 61.6 19.5 3.2 $1,103 24.0 n/a 

Idaho 65.1 -17.9 19.8 49.8 81.1 38.3 2.5 $143 20.3 0.06 

Illinois 71.8 -4.6 30.6 45.2 73.1 28.6 1.8 $3,508 25.8 0.09 

Indiana 66.4 -12.0 13.0 45.0 72.8 31.1 2.4 $370 17.7 0.07 

Iowa 67.6 -7.7 18.8 44.4 77.9 38.8 2.3 $72 20.3 0.07 

Kansas 67.3 -10.6 20.8 47.8 76.6 39.2 2.4 $577 28.5 0.10 

Kentucky 68.1 -13.5 19.1 44.4 77.5 33.7 3.4 $247 20.1 0.06 

Louisiana 69.3 -21.7 26.7 52.6 71.3 31.6 1.6 $779 19.5 0.08 

Maine 70.5 4.7 27.5 49.3 78.9 37.7 3.1 $749 19.2 0.08 

Maryland 71.6 -4.7 39.7 49.5 74.6 29.9 4.0 $6,847 31.8 0.10 

Massachusetts 71.5 -7.9 45.2 44.4 74.1 29.0 3.2 $25,739 31.0 0.14 

Michigan 69.4 -11.2 18.8 45.7 74.8 34.7 2.2 $644 22.1 0.08 

Minnesota 69.5 -10.0 22.7 53.2 72.9 35.1 3.3 $3,112 31.0 0.10 

Mississippi 62.4 -19.5 21.9 41.5 71.9 35.0 1.6 $498 19.5 0.04 

Missouri 65.6 -9.1 21.3 55.3 75.6 37.6 2.5 $2,555 22.4 0.08 

Montana 64.8 -6.4 30.5 48.7 83.4 45.0 2.3 $623 23.9 0.04 

Nebraska 71.5 -20.5 17.2 50.6 73.3 33.9 2.3 $1,160 20.9 0.10 

Nevada 63.4 -9.8 18.5 41.6 72.3 28.0 0 $543 20.6 0.11 

New Hampshire 71.7 -5.2 33.5 42.1 72.1 31.1 0.2 $7,092 26.6 0.12 

New Jersey 71.0 18.7 40.6 41.0 73.7 29.2 2.1 $5,872 30.1 0.13 

New Mexico 64.6 -23.5 34.8 58.6 77.1 38.0 2.2 $310 23.1 0.05 

New York 74.4 2.3 39.9 47.8 69.9 28.9 4.4 $4,454 26.8 0.12 

North Carolina 69.3 -17.9 18.6 52.1 72.6 31.9 3.2 $3,380 23.3 0.10 

North Dakota 66.7 -22.6 19.7 53.7 85.4 43.9 1.2 $70 22.5 0.05 

Ohio 68.0 -11.6 22.2 50.4 72.5 31.0 3.6 $1,001 21.5 0.09 

Oklahoma 70.5 -10.5 13.9 44.0 76.6 35.9 2.5 $292 20.5 0.08 

Oregon 64.3 -14.7 24.4 54.4 77.4 35.0 3.7 $2,251 27.7 0.11 

Pennsylvania 71.0 3.2 31.7 50.9 75.8 33.4 2.6 $3,852 22.1 0.08 

Rhode Island 71.2 -6.2 38.8 47.0 79.1 39.3 2.6 $1,934 27.8 0.11 

South Carolina 67.0 -17.5 22.3 54.6 76.2 34.2 2.3 $88 21.3 0.07 

South Dakota 64.8 -23.4 17.4 53.9 78.6 36.8 0 $20 21.8 0.04 

Tennessee 60.5 -19.8 21.0 49.3 74.0 36.9 0.1 $1,795 16.9 0.08 

StratEdge 18 
 

 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

Texas 67.0 -12.3 26.2 48.0 72.6 26.4 0.0 $6,765 23.3 0.09 

Utah 64.1 -5.4 21.2 38.9 72.4 33.8 2.8 $3,176 27.6 0.07 

Vermont 64.4 -12.7 28.6 38.0 78.4 42.2 2.3 $525 27.1 0.07 

Virginia 65.5 -6.8 32.3 53.2 74.0 35.8 2.9 $5,611 30.3 0.10 

Washington 64.2 -11.5 32.4 47.6 78.5 37.6 0 $4,814 28.1 0.11 

West Virginia 66.7 -18.3 15.8 51.9 78.2 40.6 2.5 $40 16.3 0.06 

Wisconsin 66.7 -7.5 17.7 51.4 77.3 39.5 3.1 $683 22.3 0.09 

Wyoming 66.8 -15.8 16.0 53.3 81.5 42.5 0 n/a 19.8 0.05 

StratEdge 19 
 

 

 

 

 

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

Appendix 3. Select Empirical Results of Analysis of 

Survival Rates of Women-Owned Businesses 

Factor Analysis of business climate variables

 Communalities 

Initial Extraction 

perstax 1.000 .110 

vcinv 1.000 .708 

weduc 1.000 .721 

bband 1.000 .716 

Extraction Method: Principal Component Analysis. 

High values of communalities (eigenvalues greater than 0.7) for the level of venture capital investment, educational 

level of the workforce and broadband penetration indicate that the three variables are highly related and hence the 

validity of using one statistical construct in place of the three variables. 

Multivariate Regression Analysis of Policy Environment and Business Climate Variables 

on WOB Survival Rates 

Coefficientsa 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients 

B Std. Error Beta t Sig.

1 (Constant) 

perstax 

REGR factor score 

1 for analysis 2 

sbclim 

61.815 

26.702 

1.261 

.106 

2.685 

46.745 

.436 

.069 

.094 

.378 

.256 

23.025 

.571 

2.890 

1.541 

.000 

.571 

.006 

.130 

a. Dependent Variable: Survival rate for WOB 1997-2001 

REGR Factor Score represents the “knowledge economy orientation,” a statistical construct derived from the 

level of venture capital investment, educational level of the workforce and degree of broadband penetration. 

StratEdge 20 
 

 

Coefficientsa

-10.860 2.593 -4.188 .000

81.735 90.333 .122 .905 .370

2.756 1.162 .352 2.371 .022

-.184 .151 -.179 -1.217 .230

(Constant)

perstax

REGR factor score

1 for analysis 2

hwymipp

Model

1

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig.

a. Dependent Variable: Percentage change in employment for WOB 1997-2001

– Explaining State-Level Differences in the Performance of Women-Owned Businesses – 

Appendix 4. Select Empirical Results of Analysis of 

Changes in Employment of Women-Owned 

Businesses 

Multivariate Regression Analysis of Policy Environment and Business Climate Variables 

on WOB Change in Employment 

Coefficientsa 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients 

B Std. Error Beta t Sig.

1 (Constant) 

perstax 

REGR factor score 

1 for analysis 2 

hwymipp 

-10.860 

81.735 

2.756 

-.184 

2.593 

90.333 

1.162 

.151 

.122 

.352 

-.179 

-4.188 

.905 

2.371 

-1.217 

.000 

.370 

.022 

.230 

a. Dependent Variable: Percentage change in employment for WOB 1997-2001 

REGR Factor Score represents the “knowledge economy orientation,” a statistical construct derived from the level of 

venture capital investment, educational level of the workforce and degree of broadband penetration. 

StratEdge 21