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Where is my partner? The role of gender in the formation of entrepreneurial businesses

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Abstract

This study examines the ownership structure of nascent businesses with a particular focus on the role of gender. Based on theories of gender and entrepreneurship, we examine how male and female entrepreneurs differentially mobilize their preexisting social and cultural capital to launch new businesses. With their limited social and cultural capital, we expect that female entrepreneurs are more likely to establish either a solo or a family-only enterprise rather than a non-family business in comparison to male counterparts. Moreover, we explore the possibility that female-led solo or family businesses tend to show lower performance compared to the male counterpart. Using a nationally representative data of nascent entrepreneurs in the USA, the results suggest that female entrepreneurs are more likely to found enterprises alone or with family members than their male counterparts especially when they lack social or cultural capital. In addition, our findings show that solo or family businesses run by female entrepreneurs tend to display lower initial performance compared to males. The results have important implications for broadening our understanding of the role of gender in the formation of entrepreneurial businesses.

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Notes

  1. 71.6% of family ties that female respondent-entrepreneurs utilize to launch businesses are spouses (i.e., husbands), the percentage of which is slightly larger than that of male entrepreneurs (63.2%) who rely on spousal relationships (i.e., wives).

  2. We examine whether entrepreneurs’ characteristics differ between the initial sample of all entrepreneurs and this sub-sample of those who have already received revenues from their business activities. The results from chi-square tests suggest that there is no difference in terms of entrepreneurs’ sex (χ2 = 1.356). At the same time, however, difference exists in terms of ownership structure (χ2 = 10.612, p < 0.05). Specifically, entrepreneurs who opened either a solo or a family business are more likely to be included in our sub-sample.

  3. As to more detailed information on firm performance, only expected annual revenue is asked in the survey.

  4. In this variable, there are a few outliers who report from 700 up to 1500 in the number of individuals they supervised. We dropped these cases in our main analysis to check for robustness of results, and the results do not change in any qualitative sense.

  5. Regarding the gender difference in industrial types, the chi-square analysis shows that females are more likely to open businesses in the sector of retails (e.g., retail store, restaurant, bar, or nightclub), service (e.g., health, education, social, business), and finance (e.g., finance, insurance, real estate), while males tend to engage in manufacturing, construction, agriculture/mining, and transportation sectors (χ2 = 45.34***). As to ownership type, solo enterprises are more likely to be established in service, construction, and finance, while family enterprises are more often organized in agriculture and mining (χ2 = 47.42***).

  6. The variable with the highest VIF is the White variable; the VIF score for the White variable is 2.51.

  7. We include all three interaction terms in a single saturated model since the correlation among any pairs of the interaction terms is very weak. None of the correlation coefficients exceed 0.15. In addition to our saturated model, we include each interaction terms in separate models and, as expected, the results do not change in any qualitative sense.

  8. A majority of family ties that the respondent entrepreneurs rely on are spouses (66.6%). In a separate analysis, we explored the possibility that partnership with a spouse is different from partnership with family members in general. The dependent variable is whether the respondents specifically recruit their spouses to establish businesses. The results are not significantly different from our main model. The effect size slightly decreases from 1.781 times (p < 0.05) for family members in general to 1.520 times (p < 0.05, one-tailed) for spouses only.

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Acknowledgments

We are grateful to M. Diane Burton, Brian Rubineau, Wesley Sine, Pam Tolbert, and the project members of Creativity, Innovation, and Entrepreneurship in the Institute of Social Science at Cornell University for their valuable comments. We also thank anonymous reviewers for their helpful and insightful suggestions.

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Authors and Affiliations

  1. Department of Organizational Behavior, The ILR School, Cornell University, Ithaca, NY, USA

    Yisook Lim

  2. Department of Sociology, Chung-Ang University, Seoul, Republic of Korea

    Chan S. Suh

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  1. Yisook Lim
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Correspondence to Chan S. Suh.

Appendices

Appendix 1

Table 6 Correlations among independent variables
Full size table

Appendix 2

We analyze the SBO dataset in order to test if our findings on nascent businesses using PSED II can be replicated in the case of new businesses that have already been launched. The SBO dataset covers all non-farm businesses that file tax forms to the Internal Revenue Service (IRS). Therefore, the data provides very comprehensive information on the socio-demographic characteristics and entrepreneurial activities of business owners in the USA. In order to match with the PSED II that was collected between 2005 and 2006, we use the SBO’s Public Use Micro-data Sample (PUMS) that was surveyed in 2007. Within this data, we limit our analysis to young firms with short business histories (5 years or less), and our sample includes over 300,000 firms. Since the data does not include detailed information on the backgrounds and the social relationships of business owners, we fail to include a few important control variables in our models. Despite its shortcomings, however, we believe that the supplementary analysis on the SBO PUMS data can serve as a robustness check for our main analysis using PSED II. While information on the social relationships among business owners is limited, we can at least examine the association between the owners’ sex and the basic ownership structure of their businesses.

In this supplementary analysis, we employ multinomial logistic regression models to explain the determinants of different ownership structure of new businesses. The SBO data provides information on whether the respondents run their businesses by themselves and, if not, whether their businesses are co-owned by family members or not. While we are unable to distinguish between family enterprises and mixed enterprises due to lack of information, we are able to distinguish among solo, family, and non-family enterprises. Among 335,501 young businesses in the data, 197,848 respondents (59.0%) are running solo enterprises, 86,259 respondents (25.7%) own their businesses with at least one family member, and 51,394 respondents (15.3%) run their businesses only with non-family members.

The key independent variable in our analysis is entrepreneurs’ sex. In addition to our main variable, we control for entrepreneurs’ socio-demographic characteristics that are available in this dataset such as race (Whites, Blacks, Hispanics, and others), age, and educational backgrounds (less than college education, college education, and graduate education). We also control the respondents’ prior entrepreneurial experience as a proxy for their professional career and experience. Due to the lack of information in the SBO data, however, we are unable to include the respondents’ extent of social and cultural capital, their marital status, and their status of having a child as control variables in our models. The descriptive statistics of the variables we use in the SBO PUMS data is presented in Table 7.

Table 7 Descriptive statistics of dependent and independent variables: the SBO PUMS dataset (N = 335,501)
Full size table

Next, we run multinomial logistic regression models to test the effect of entrepreneurs’ sex on the ownership types of new businesses. We measure the likelihood that an entrepreneur runs either a solo enterprise or a family enterprise relative to running a non-family enterprise. Family enterprises include entrepreneurial teams with at least one family member, while non-family enterprises means teams with no family member. We use robust standard errors to adjust the clustered feature of the data by industrial categories. We do not include industrial categories as a series of binary independent variables in our supplementary analysis, since the number of industrial categories in the SBO data, following the North American Industry Classification System (NAICS) code, is much larger than that in the PSED II. Results from our regression analysis are presented in Table 8.

Table 8 Multinomial logistic regression on the relationship between entrepreneurs’ sex and business type using the SBO PUMS dataset
Full size table

The results are consistent with our main findings on the gendered formation of nascent businesses from the PSED II dataset, namely that female entrepreneurs are more likely to open either a solo enterprise or a family enterprise rather than a non-family business relative to male entrepreneurs. The results indicate that being a female entrepreneur is positively associated with founding a solo enterprise by 2.625 times (b = 0.965, p < 0.001). Also, being a female is positively associated with a 1.476 times increase (b = 0.389, p < 0.001) in founding a family business relative to opening a non-family business. Thus, the results support our expectation that female entrepreneurs are more likely to establish either a solo or a family enterprise.

Among the control variables, our findings on the effect of previous self-employment experience are noteworthy. The results show that having prior experience as an entrepreneur is associated with a 0.495 times decrease (b = − 0.684, p < 0.001) in founding a solo enterprise and with a 0.296 times decrease (b = − 0.351, p < 0.001) in opening a family enterprise in comparison to a non-family enterprise. Thus, experienced entrepreneurs are more likely to recruit a non-family member in forming an entrepreneurial team. Regarding entrepreneurs’ race, the results indicate that Whites are significantly less likely to open a solo business, while Blacks and Hispanics are more likely to do so. Also, Black entrepreneurs are less likely to form a family business than those in the other race category. In terms of age, our findings show that entrepreneurs are less likely to open a solo business and more likely to found a family business as they get older. Finally, one’s years of education also matter in the ownership structure of new businesses. The results show that entrepreneurs with college or graduate education are more likely to form an entrepreneurial team with non-family members rather than a solo or a family enterprise. All in all, the results from our supplementary analysis corroborate our findings on the relationship between entrepreneurs’ sex and the ownership structure of new businesses.

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Lim, Y., Suh, C.S. Where is my partner? The role of gender in the formation of entrepreneurial businesses. Small Bus Econ 52, 131–151 (2019). https://doi.org/10.1007/s11187-018-0027-3

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