Under contract number SBAHQ-06-M-0480 Previous research has shown the performance of women-owned firms lagging male-owned firms on factors such as annual sales, employment growth, income, and venture survival. Reasons for the differences are often hypothesized, but empirical tests have historically suffered from data with a limited number of control variables on the motivations and characteristics of the owners. Moreover, many of the previous studies have suffered from survivor bias as they study existing (or surviving) businesses.
This study seeks to determine why a performance difference exists for female- and male-owned ventures. Overall Findings When other factors are controlled for, gender does not affect new venture performance. However, several factors—differing expectations, reasons for starting a business, motivations, opportunities sought and types of businesses—vary between the genders, and these result in differing outcomes. Such observations should be taken into account when comparing the outcomes of ventures across genders. Highlights
While gender was shown not to affect new venture performance when preferences, motivation, and expectations were controlled for, the differences observed among men’s and women’s new business ventures include the following: • Men had more business experience prior to opening the business and higher expectations. • Women entrepreneurs had a larger average household size. • The educational backgrounds of male and female entrepreneurs were similar. • Women were less likely than men to purchase their business. • Women were more likely to have positive revenues, but men were more likely to own an employer firm. Female owners were more likely to prefer low risk/return businesses. • Men spent slightly more time on their new ventures than women. • Male owners were more likely to start a business to make money, had higher expectations for their business, and did more research to identify business opportunities. • Male entrepreneurs were more likely to found technologically intensive businesses, businesses that lose their competitive advantage more quickly, and businesses that have a less geographically localized customer base. • Male owners spent more effort searching for business opportunities and this held up when other factors were controlled for. Differences between women and men concerning venture size and hours are explained by control variables such as prior start-up and industry experience. • Researchers and policymakers need to understand that studies which do not take into account the differing nature of men- and women-owned firms could result in misleading results. Scope and Methodology The data used was from the Panel Study of Entrepreneurial Dynamics (PSED).
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The PSED captures very small ventures on average and is a self-reported survey. The subset used was a sample
This report was developed under a contract with the Small Business Administration, Office of Advocacy, and contains information and analysis that was reviewed and edited by officials of the Office of Advocacy. However, the final conclusions of the report do not necessarily reflect the views of the Office of Advocacy. of representative entrepreneurs who started in 1998 and 1999, resulting in 685 usable new businesses. Women represented 349 cases as they were oversampled, and the data were weighted to account for the oversampling. Various measures of performance outcome were studied, such as venture size.
Many of the variables were on a scale from “no extent” to “a very great extent. ” Econometric models were created to determine the relationship among the variables. The relatively small sample size, short time frame, and nascent nature of the ventures are limitations of the study. This report was peer reviewed consistent with the Office of Advocacy’s data quality guidelines. More information on this process can be obtained by contacting the director of economic research at gov or (202) 205-6533. Ordering Information The full text of this report and summaries of other studies performed under contract with the U.
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Having been looking for a college that would suit me as a student athlete, I turned to some of my senior of my seniors would had already entered college and had gone through the same process. I got impressive feedback but it was not until I received an email from Coach Jason Shaver with a link to UCCS website that my eyes opened up to the school I have known that I would like to study Business ...
S. Small Business Administration’s Office of Advocacy are available on the Internet at www. sba. gov/advo/ research. Copies are available for purchase from: National Technical Information Service 5285 Port Royal Road Springfield, VA 22161 (800) 553-6847 or (703) 605-6000 TDD: (703) 487-4639 www. ntis. gov Order number: PB2007-112634 Paper A05 ($31. 50) Microfiche A01 ($14. 00) CD-ROM A00 ($24. 00) Download A00 ($18. 95) For email delivery of Advocacy’s newsletter, press, regulatory news, and research, visit http:// web. sba. gov/list. For Really Simple Syndication (RSS) feeds, visit www. sba. ov/advo/rsslibrary. html. I. EXECUTIVE SUMMARY This report describes a statistical evaluation of the similarities and differences between male and female entrepreneurs and their ventures. The purpose of the study was to gain a better understanding of the extent to which entrepreneurship by men and women is different. Using data from the Panel Study of Entrepreneurial Dynamics, the sample included 685 new business people who indicated that they were in the process of starting a business in 1998 or 1999. Preferences, motivations and expectations are not randomly distributed across gender.
Analyzing the effect of structural barriers on new venture performance requires precise measurement of the effects of gender on these things. Failure to measure the effect of preferences, motivations and expectations, or inaccurate measurement of the effect of these factors, will lead to biased estimates of the effects of structural barriers, and over- or under-estimation of their effects. Our study contributes to the literature on gender differences in entrepreneurship by showing the presence or absence of support for many previous findings about gender differences in entrepreneurial activity.
Specifically, the more limited findings shown in the PSED, in comparison to previous studies, indicate the limitations of previous studies, and suggest caution in assuming their validity. First, because the PSED is a survey of a representative sample of people in the process of starting new businesses in the United States differences between the findings here and those of prior studies might exist because the results of previous studies are artifacts of recall and selection bias.
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Second, differences might result from differences in “self-employment” on the one hand, and business formation on the other. Third, the differences might be explained by selection bias in previous studies, like the Survey of Small Business Finances, which survey surviving small businesses. Fourth, the differences might exist because of unobserved heterogeneity in previous studies that examine data sources like the Statistics of Income and the Current Population Survey, which include a limited number of variables.
Our study makes several contributions to public policy. First, it shows that there is no evidence in the PSED for the effect of gender on new venture performance when preferences, motivation and expectations are controlled. Second, the study provides information useful to policy makers who seek to analyze whether government intervention is needed to overcome structural barriers to female preferences, motivations, and expectations for new ventures. 2 II.
BACKGROUND A wide variety of research studies have shown differences between male and female entrepreneurs: motivations for starting businesses; their preferences for venture risk; the types of businesses they start; the process they use to identify business opportunities; the size of their start-ups; the effort they expend in developing of those businesses; their confidence in their start-up efforts; their start-up problems; their expectations for the performance of their businesses; and their performance outcomes (Brush, 1992; Brush et al. , 2006; Carter et al. , 1997; Du Rietz 1999; Fischer 1992; Rosa et al. 1996; Sexton and Bowman-Upton, 1990; Van Stel 2003; and Verhuel et al. , 2004).
Despite the wealth of studies on this topic, some researchers have questioned how much we understand about the similarities and differences between male and female entrepreneurs because of limitations of the data that have been used to explore this question. These data are limited in four ways. First, many sources of data, such as the Current Population Survey, examine “self-employment. ” While selfemployment is an important phenomenon, it is not a good proxy for new business creation.
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Many selfemployed people do not create new businesses, and many people who create new businesses are categorized by the Census Bureau as “wage employed” because wage employment is their primary source of income (Reynolds, 2000).
Second, many sources of data are quite limited in the information that they include. As a result, analysis of these data cannot control for many other factors, making the results subject to the limitation that gender may merely be proxying for unobserved characteristics that really account for the differences observed in the data.
Third, many data sources fail to measure entrepreneurial activity from the beginning of the process – that is, when a person begins to create a new firm. As a result, the data collected from this process is highly selective (only entrepreneurs running surviving firms are contacted).
Moreover, the data collection process involves a great deal of recall bias, as entrepreneurs are asked to reconstruct events, attitudes, and motivations that took place months, and often years, before (Reynolds, 2000).
Finally, many data sources are convenience samples that are not representative of the underlying U. S. population. This is necessarily the case when studies use sampling frames – Dun and Bradstreet listings, unemployment insurance filings, new incorporations, trade association membership, and affiliation with a university – which are not representative of the overall population. As a result, one cannot draw inference from the results of these studies to the overall population of start-ups in the United States.
The data limitations make it difficult for policy makers to develop effective policies toward entrepreneurship because they cannot have confidence in the data on which they are making policy choices. Because researchers cannot undertake randomized experiments to discriminate against groups of entrepreneurs, gender-related policy questions in entrepreneurship need to be addressed through regression 3 analysis. Accurate regression analysis depends on the ability to measure and account for a variety of factors that could account for gender differences that policy intervention is designed to remedy (e. . , discrimination in capital markets).
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... of Men, Women and successful entrepreneurs. Journal of Business venturing, 3 (3): 249-258 Buttner, E. Holly (1993). Female entrepreneurs: How Far have they come? Business Horizons, ... (1991) noted that 60% of women entrepreneurs had operated their businesses for 1 to 5 years. Most women entrepreneurs started their businesses from scratch (Nelton, 1989), created ...
Given that the multitude of factors which needed to be ruled out are almost never randomly distributed across gender (e. g. , motivations for starting a business, effort expended, expectations for performance or the types of businesses started), measuring gender differences in these alternative factors is essential to determining if discrimination adversely affects female entrepreneurs. Failure to measure them will likely lead to biased estimates, which could result in the over- or under-estimation of the need for policy intervention.
Moreover, even if the variety of factors that could account for the gender differences that the policy intervention is designed to address are controlled, accurate policy can be formulated on the basis of those findings only if the phenomenon of new business formation (and not something else, like self-employment) was examined in the studies, and only if the samples in the studies represent the population that the policy would effect, in this case the overall United States.
This study uses a new data set, called the Panel Study of Entrepreneurial Dynamics (PSED), to examine many of the differences between male and female entrepreneurs documented in the literature. The PSED examines new firm formation from the inception of the firm formation process for a sample of U. S. entrepreneurs that matches the distribution of the Current Population Survey in terms of gender, race, age, and income. Consequently, the PSED allows exploration of the difference between men and women in new business creation without being subject to many of the limitations (described above) that come from the examination of other data sets.
As a result, this study permits examination of the differences between male and female entrepreneurs, which is necessary for policy makers to determine if policy intervention is needed, and, if so, what types of interventions (e. g. , laws to prevent discrimination, education and support programs, and so on) should be used to alleviate the problems. It is important to note that the PSED suffers from its own limitations. First, the PSED data are obtained through a survey, and the self-reported data are not verified through corroboration with another data source.
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... Male-female competition-The male-female competition is another factor, which develop hurdles to women entrepreneurs in the business management process. Despite the fact that women entrepreneurs ... in the future when they start their business 13. 81.25% women entrepreneur employed less than 5 employee ... degree on management. This brilliant corporate woman started her career in Boston Consulting Group ...
Therefore, the answers given by respondents to the PSED may not be entirely accurate and might only reflect differences in the way in which male and female entrepreneurs answer questions. Second, the small sample size and short time horizon of the PSED might account for many of the null findings. Because many of the differences between male and female entrepreneurs discussed in the literature come from the examination of very large datasets, substantive differences between entrepreneurs of the two genders may not be statistically significant in the PSED because the sample size is very small.
In addition, because the PSED begins with the examination of people in the process of starting a business and gathers data only over four years, it may not show differences that become visible only after the businesses age. 4 Third, the PSED mixes entrepreneurs with people who say they are starting a business, but fail to do so within the study period. Only one-third of the PSED sample has a business that the respondent views as “up-and-running” at any time during the four-year observation period.
This small proportion suggests that the majority of the sample consists of people who never actually get a new business started. The inclusion of these people in the sample might explain the divergence of the findings from those observed in other datasets that look only at employer firms or people for whom self-employment is their primary occupation. Fourth, differential selection into starting a business might account for many of the patterns observed among male and female entrepreneurs because men are twice as likely as women to start businesses.
Therefore, women entrepreneurs might not differ much from male entrepreneurs, because a more selected group of women start businesses. If women started businesses at the same rate as men, then more differences between male and female entrepreneurs might be observed. In the next section, we briefly review the findings of previous researchers about gender differences on a variety of dimensions of entrepreneurial activity and use them to formulate hypotheses that we would expect to be supported in our investigation. III.
PREVIOUS RESEARCH & HYPOTHESES Previous studies have shown that male and female entrepreneurs differ in terms of their business outcomes; the motivations they have for starting businesses; the effort that they put into the development of their businesses; the size of their start-ups; the types of businesses they start; the performance expectations they have for their businesses; their preferences for venture risk; the process through which they identify business opportunities; the confidence they have in their start-up efforts; and the start-up problems that they face (Brush, 1992; Brush et al. 2006; Carter et al. , 1997; Du Rietz 1999; Fischer 1992; Rosa et al. , 1996; Sexton and Bowman-Upton, 1990; Van Stel 2003; and Verhuel et al. , 2004).
We review each of these topics in turn, and then posit ten hypotheses for the current study. III. 1 Firm Performance Studies have shown that the performance of female-led new ventures lags behind that of male-led new ventures. Sales growth, employment, employment growth, income, and venture survival are all lower for female-led ventures (Boden, 2000; Office of Advocacy, 2006; Robb and Wolken 2002; Srinivasan, et al. 1993).
Women-owned businesses have lower sales and employ fewer people than men (Fischer et al. , 1993).
For instance, in 2002 women-owned employer firms generated an average of $87,585 in sales and had an average of 7. 79 employees, as compared to $1,862,159 in sales and 12. 04 employees for those owned by men (Office of Advocacy, 2006).
Women-owned businesses are less profitable than those started by men. The average employer-firm owned by a woman generates only 78 percent of the profit of the comparable business owned by a man (Robb and Wolken, 2002).
Moreover, 46 percent of self-employed women had an 5 income of less than $15,000 in 1998, while only 21 percent of self-employed men earned this low level. In contrast, 16 percent of self-employed men earned more than $95,000, as compared to 4 percent of women (Office of Advocacy, 2001).
New ventures started by women are less likely to survive over time than new ventures started by men. The four-year survival rate of new women-owned employer firms is 8. 6 percent lower than that of comparable new businesses founded by men (Boden, 2000; Srinivasan et al, 1993).
Finally, research from Norway shows that new ventures started by women are slower to complete the organizing activities necessary to get their businesses “up-and-running” (Alsos and Ljunggren, 1998).
These observations lead to our first hypothesis: Hypothesis1: The performance of new ventures led by female entrepreneurs is lower than the performance of ventures led by male entrepreneurs. III. 2 Motivations for Starting the Business People start businesses for a variety of different reasons; and these motivations vary by gender.
First, women are more likely than men to start businesses to achieve a work-family balance (Brush et al. , 2006).
In surveys, women cite the desire for flexibility and work-family balance as a reason they started their businesses more often than men (Boden, 1999; Carter et al. , 2003).
Parenthood plays a significant role in women’s desire to become self-employed (Birley 1989).
Research shows that many women want to become self-employed to develop a more flexible work schedule that allows them to balance work and family demands (Boden 1996; Georgellis and Wall, 2004; Lombard 2001).
They also pursue selfemployment because it allows them to work at home; and may ease the burden of finding childcare (Boden 1996; Connelly 1992; Presser and Baldwin, 1980).
In fact, Boden (1996) uses data from the Current Population Survey to show that, there is a significant positive correlation for women between entrance into self-employment and becoming a parent. Second, male entrepreneurs are much more likely than female entrepreneurs to say that the desire to make money or build a company were the reasons why they started their businesses.
For instance, DeMartino and Barbato (2003) found that male entrepreneurs prefer careers that make money, while female entrepreneurs prefer careers that allow work-family balance. Women also place higher value on nonfinancial dimensions of employment than men do (Jurik, 1998).
They are more likely than men to cite personal interests, a desire for self-fulfillment, and job satisfaction as their reasons for starting businesses (Georgellis and Wall, 2004; Jurik 1998).
Women are also more likely than men to say that they started their businesses to be challenged personally and to achieve self-determination (Buttner and Moore, 1997).
Third, women are more likely than men to start businesses to gain the recognition of others (Fischer et al. , 1993).
Shane et al (1991) found that women in the United Kingdom and in Norway are more likely than men in those countries to start businesses to “achieve something and get recognition for it” (page 438).
6 These observations lead to our second hypothesis: Hypothesis 2: Male and female entrepreneurs start businesses for different reasons. III. 3 Effort Expended on New Business Creation Researchers have found that female entrepreneurs, on average, work fewer hours than male entrepreneurs.
In particular, studies have shown that women invest less time in the development of their new businesses than men (Verheul et al. , 2004).
They also indicate that self-employed women are less likely to work full-time than self-employed men (OECD, 1998).
Male entrepreneurs may work more hours than female entrepreneurs because they are more likely to have gone into business to earn money. Alternatively, they may face fewer competing demands for their time because women devote more hours to caring for children, older parents, and the household.
Competing domestic demands may restrict the time and effort that women can devote to other things, such as venture formation, leading women to spend less time on their new ventures than their male counterparts. This leads to our third hypothesis: Hypothesis 3: Male entrepreneurs spend more hours on the development of their ventures than female entrepreneurs. III. 4 Venture Size Women start businesses that are smaller than those started by men. Studies of surviving businesses show that those that are women-owned are smaller than those that are men-owned (Kalleberg and Leicht, 1991).
In addition, women start businesses with lower levels of initial employment and capitalization than men (Brush, 1992; Carter et al. , 1997).
The smaller scale of female-led start-ups is believed to be the result of a lack of access to larger-scale business opportunities and the financial resources necessary to develop them (Reynolds, forthcoming) and different goals and intentions for their businesses (Carter and Allen, 1997).
These observations lead to our fourth hypothesis: Hypothesis 4: Male entrepreneurs start businesses of larger magnitude than female entrepreneurs. III. Type of Business Started Male and female entrepreneurs do not start the same types of businesses. Female-led businesses are more likely to be found in personal services and retail trade and less likely to be found in manufacturing and high technology (Anna et al. , 2000; Brush et al. , 2006).
In addition, women start businesses that are less 7 growth-oriented and less driven by opportunity, and more oriented toward wage substitution (Minniti et al, 2005).
Some researchers argue that gender differences in the types of businesses that men and women found are the result of socialization and structural barriers.
Women tend to work in certain occupations and industries because these occupations and industries are more socially acceptable for women, and because women face obstacles to working in other industries and occupations (Mirchandani, 1999).
Because entrepreneurs tend to identify opportunities to start businesses that are similar in type and industry to those in which they previously worked, the tendency of women to work in certain types of businesses leads them to start those types of companies. In addition, some businesses cannot be founded easily by people without the appropriate educational background.
This is particularly the case for businesses that rely heavily on technology. Because women are less likely than men to study engineering or science (Brush et al. , 2006), they often lack the education to start businesses that demand technical skills. Furthermore, some businesses are inherently easier to start than others because they have lower barriers to entry. Women may be more likely to start businesses that face low barriers to entry because these businesses make lesser demands on human or financial capital than other businesses, and women may lack these types of capital.
These observations lead to the following hypothesis: Hypothesis 5: Male and female entrepreneurs start different types of businesses. III. 6 Expectations for Venture Performance Female entrepreneurs have lesser expectations for their businesses than male entrepreneurs. First, they expect to generate lower profits and employ fewer people than male entrepreneurs because they are less highly motivated to make money and more motivated to achieve other goals (Brush, 1992).
Second, male entrepreneurs have greater confidence in their entrepreneurial abilities than female entrepreneurs.
These differences in confidence lead male entrepreneurs to form greater expectations for their businesses. Third, female entrepreneurs tend to start types of businesses that have lower growth and income potential than male entrepreneurs. As a result, the expectations of female entrepreneurs, which are in line with the reality of the businesses that they start, are lower than those of male entrepreneurs. Fourth, female entrepreneurs are more likely to set limits beyond which they do not want to expand their businesses to ensure that they do not adversely affect their personal lives (Cliff, 1998).
Fifth, female entrepreneurs start smaller scale businesses than male entrepreneurs; hence their initial expectations for their businesses tend to be lower (Anna et al, 2000).
These observations lead to our sixth hypothesis: 8 Hypothesis 6: Male entrepreneurs have greater performance expectations for their businesses than female entrepreneurs. III. 7 Risk Preferences Research in sociology and psychology shows that women are more risk averse than men across a wide variety of settings (Arch, 1993; Byrnes et al. , 1999).
In particular, women display greater financial risk aversion than men (Jianakoplos and Bernasek 1998).
Some studies suggest that this greater risk aversion carries over to female entrepreneurs. In fact, one study shows that a convenience sample of female entrepreneurs have lower risk propensity scores than male entrepreneurs on a psychological scale (Sexton and Bowman-Upton, 1990).
The greater risk aversion of female entrepreneurs is thought to make them less willing to trade potential gain for risk, which leads them to prefer businesses with lower failure probabilities than those preferred by male entrepreneurs (Brush et al. , 2006).
As a result, male entrepreneurs pursue business opportunities that involve more risk than the opportunities pursued by female entrepreneurs (Baker et al. , 2003).
The greater risk aversion of female entrepreneurs also leads them to engage in greater amounts of risk minimizing activity. For instance, Mallette and McGuiness (2004) found that the female entrepreneurs focus more on minimizing risk than male entrepreneurs in the business organizing process. These observations lead to our seventh hypothesis: Hypothesis 7: Male entrepreneurs prefer riskier ventures than female entrepreneurs. III. Opportunity Identification Female entrepreneurs search for new business opportunities differently than male entrepreneurs for a variety of reasons. First, many opportunities are identified through information that is transferred through social networks. Women have different types of social networks than men (Renzulli et al. , 2000).
As a result, they have access to different sources of information about opportunities. For instance, male entrepreneurs are more likely than female entrepreneurs to identify opportunities through conversations with investors and bankers because, on average, they know more investors and bankers.
Second, learned behaviors and social norms lead men and women to develop different cognitive processing styles (Gatewood et al. , 1995).
As a result, on average, men and women gather information and solve problems differently (White et al. , forthcoming) For instance, female entrepreneurs are thought to learn from a greater variety of sources than male entrepreneurs, while male entrepreneurs are thought to learn more from setbacks than female entrepreneurs (Barrett, 1995).
In addition, the greater risk aversion of female entrepreneurs may lead them to search for more information that mitigates the potential risks about business opportunities than their male counterparts (Eckel and Grossman, 2003).
9 These observations lead to the eighth hypothesis. Hypothesis 8: Male and female entrepreneurs identify business opportunities differently. III. 9 Confidence in Organizing Abilities Because of how men and women are socialized, women have lower levels of career-related selfefficacy than men, particularly in careers that are seen as traditionally “male” (Brown, 2002).
Because starting a business has been considered a traditionally “male” career, female entrepreneurs are thought to have less confidence in their entrepreneurial abilities than male entrepreneurs. As a result, they are less likely to believe that they can undertake the key tasks in organizing a new venture, such as obtaining start-up and working capital, and attracting customers. This argument leads to our ninth hypothesis: Hypothesis 9: Male entrepreneurs have more confidence in their abilities to organize their new ventures than female entrepreneurs. III. 0 Start-up Problems Research suggests that social norms about the role of women in society, the shortage of female role models, and the greater household burdens faced by women lead female entrepreneurs to face more start-up problems, and for those problems to be of greater magnitude, than their male counterparts. For instance, attitudes toward the role of women make it more difficult for female entrepreneurs to be taken seriously as business people (Brush, 1992), and to gain support for their entrepreneurial activities from their spouses, family, and friends (Stoner et al. 1990).
In addition, the relative shortage of female role models makes it more difficult for female entrepreneurs to obtain adequate mentorship for their start-up efforts. Furthermore, the greater household and childcare responsibilities of women lead them to have more trouble balancing business formation and family responsibilities (Stoner et al. , 1990).
These observations lead to the tenth hypothesis: Hypothesis 10: Female entrepreneurs face more start-up problems than male entrepreneurs. IV. DATA & RESEARCH METHODOLOGY IV. Sample We use data from the Panel Study of Entrepreneurial Dynamics (PSED) to conduct the analysis that is discussed in this report. The PSED is a multi-year effort to follow a representative sample of people who 10 were involved in the business formation process in 1998 and 1999 (Reynolds, 2000).
To create a sample representative of people in the lower 48 states who were involved in the business formation process at this time, researchers contacted 64,622 U. S. households between July of 1998 and January of 2000 through random digit dialing (Reynolds and Curtin, 2004).
They screened the first adult who agreed to participate, subject to quotas to ensure an equal number of men and women (Reynolds and Curtin, 2004).
In the screening process, three telephone attempts were made to contact blocks of 1,000 potential respondents over a three-day period, with the same number of people contacted on weekdays and weekends (Reynolds, 2000).
People who were contacted were identified as being entrepreneurs if they answered ‘yes” to the question: “Are you, alone or with others, now trying to start a new business? ” The entrepreneurs were offered $25 to participate in an hour-long telephone phone survey.
Approximately 71 percent agreed to participate, and could be contacted (Reynolds and Curtin, 2004).
However, 27 percent of the respondents were eliminated from the survey because the respondents indicated that their businesses already had positive cash flow for three consecutive months, which the PSED researchers considered to be “beyond the start-up phase” (Reynolds, 2000).
Those respondents who completed the telephone survey were offered another $25 if they would also complete a mail survey, which 72 percent did (Reynolds and Curtin, 2004; Reynolds, 2000).
These respondents were also re-contacted 12, 24 and 36 months later for follow-up telephone and mail surveys. The respondents were treated as key informants about their new ventures. Information was collected about a wide variety of topics, including demographic characteristics; background and experience; motivations, beliefs, and attitudes; perceptions of new ventures and the environment; new venture strategy; organizing activities; the achievement of milestones; financial investments made in the ventures; performance expectations; the use of assistance and educational programs; and a variety of other topics (Reynolds, 2000).
The sample included 830 people who indicated that they were in the process of starting a business in 1998 or 1999. However, the analysis reported here is limited to the 711 respondents that were in the process of starting a business that was independent of their employer’s company. An additional 26 respondents indicated that the start-up effort was not active in the past 12 months and were dropped from the sample. As a result, the usable sample includes 685 new businesses, 481 of which completed the additional mail survey.
Due to over-sampling of women, 349 of the entrepreneurs were female. Because the research effort over-sampled women and minorities, and because some categories of respondents were less likely than others to be reached or to respond to the surveys, post-sampling stratification weights were used to match the data to the Current Population Survey on gender, age, education, and race (Reynolds and Curtin, 2004).
In the analysis presented here, the weights were recentered on 1. for the usable subsample to avoid biases in the standard errors (Reynolds, 2000).
11 IV. 2 Analysis The data were examined in two ways. First, descriptive statistics were examined to compare male and female entrepreneurs on a variety of dimensions that correspond to the arguments made in the literature. In addition, the differences in the variance in characteristics across male and female entrepreneurs were explored, both to explain those differences, and to ensure that subsequent regression analysis to examine these differences is done accurately.
Second, the effect of gender on those dimensions for which bivariate analysis shows statistically significant and substantive differences were examined in a regression framework that controls for a variety of factors that might alternatively account for the differences between male and female entrepreneurs on these dimensions. Given the nature of the dependent variables, two types of regression models were used. For the continuous variables, we used ordinary least squares regression. For the dichotomous variables, we used logistic regression. IV. Variables The study involved the examination of the effect of gender on several different dependent variables and employed a variety of control variables. These variables are described below, with the dependent variables described first. IV. 3. a Dependent Variables We examined several different dependent variables to measure the factors posited in the ten hypothesis: performance outcomes; effort expended; motivations to start a business; venture size; the nature of the venture; expectations for income and employment; risk preferences; approach to opportunity identification; confidence in venture organizing; and start-up problems faced.
We begin with performance outcomes. IV. 3. b Performance Outcomes We examined seven different measures of performance: venture termination; becoming an employer firm; level of employment achieved; achievement of first sale; achievement of positive cash flow; count of organizing activities undertaken; and perception that the venture is “up and running. ” Because the PSED involves the collection of follow-up data on new ventures at three additional times after the initial interview, it is possible to look at performance outcomes that occurred over a four-year period.
We measured venture termination with a dummy variable of one if the effort to develop the venture was stopped by all parties working on it at any time in four years covered by survey. Specifically, termination will be identified as occurring when the respondent answers “no” to the PSED question: “Are you, or is anyone else, still actively pursuing the creation of this venture? ” 12 The U. S. government measures new employer firms (which it defines as firms that have at least one employee, including the founders) based on unemployment insurance filings.
Because many new ventures do not become employer firms, this status is an important milestone in the lives of new ventures. We measure employer firm with a dummy variable of one if the venture paid unemployment insurance taxes at any time in four years covered by survey. We measure employment as the number of non-founder employees of the venture at the end of the four years covered by survey. If the venture is terminated, its employment is coded as “zero. ” If the venture is alive, but the respondent does not provide the number of employees in that year, we take the latest available employment provided by the respondent.
We calculate part-time employment as one-half of full time employment. Because the employment number is skewed, in the analysis we also use the natural log of the employment plus one. For the same reason, we also operationalize employment with a dummy variable of one if the venture has any employees other than the founder and zero if it does not. We measure has revenues with a dummy variable of one if the venture receives income from the sale of goods or services at any time in four years covered by survey.
