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Gender Bias in Entrepreneurship: What is the Role of the Founders’ Entrepreneurial Background?

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Abstract

We examine the issue of entrepreneurial gender bias by focusing on the underlying mechanisms that impact the likelihood of receiving external venture-capital financing. We claim that gender bias negatively affects socially attributed dimensions (such as the stigma ascribed to entrepreneurs who have previously suffered a failure), while it has no effect on objective dimensions (such as the experience gained by entrepreneurs). Our results, based on 2088 US firms, show that female entrepreneurs are less likely to attract external funds if they have previously encountered failure. This negative effect becomes less impactful when novel or serial successful entrepreneurs are considered. Consequently, novel or serial successful entrepreneurs are expected to suffer less from gender bias if compared to peers who experienced a failure during their entrepreneurial career.

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Notes

  1. It is perhaps worth noting that some scholars argue that social stigma, under some circumstances, may trigger epiphanies and deep personal insights, turning entrepreneurs’ view of failure from a negative to a positive life experience (Singh et al., 2015). However, in this paper, we are not interested in how the social stigma is perceived by the entrepreneur. Rather, we focus on the perception and assessment of external actors (venture capitalists, business angels, etc.).

  2. Crunchbase has recently become a point of reference for professionals seeking to invest in startups and represents a primary source for several top-tier entrepreneurship-related research projects (Alexy et al., 2012; Block and Sandner, 2009; Ter Wal, 2016).

  3. Please note that our selection criteria prescribed that the first “failed” firm had to be established in the US but it seldom happened that the subsequent startup was founded in another country. This explains a minor presence of non-US firms in our treatment sample.

  4. Coarsened Exact Matching (CEM) is a novel causal inference technique used to non-parametrically create a matched dataset to evaluate the effects of a treatment. Meanwhile, the more established Propensity Score Matching (PSM) technique aims to estimate the effects of a treatment by exploiting the covariates that predict receiving the treatment.

  5. The four sectorial dummy variables aggregate 47 sectors: Administrative Services, Advertising, Agriculture and Farming, Apps, Artificial Intelligence, Biotechnology, Commerce and Shopping Clothing and Apparel, Community and Lifestyle, Consumer, Electronics, Consumer Goods, Content and Publishing, Data and Analytics, Design, Education, Energy, Events, Financial Services, Food and Beverage, Gaming, Government and Military, Health Care, Hardware, Information Technology, Internet Services, Lending and Investments, Manufacturing, Media and Entertainment, Mobile, Messaging and Telecommunications, Music and Audio, Natural Resources, Navigation and Mapping, Payments, Platforms, Privacy and Security, Professional Services, Real Estate, Sales and Marketing, Science and Engineering, Software, Sports, Sustainability, Transportation, Travel and Tourism, Video, and High Technology. We regroup these sectors into Service, High Technology, Primary, and Leisure.

  6. The estimated models in the robustness analysis have similar Bayesian Information Criteria (BIC) to the benchmark models. Results are available upon request.

  7. As done for Table 5, we display only the marginal interaction effects between the Female dummy variable and the subgroup. According to BIC, there is not a big difference among the proposed specifications. Results are available upon request.

  8. Authors are thankful to an anonymous Reviewer for suggesting us this explanation.

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Authors and Affiliations

  1. University College Dublin, Lochlann Quinn School of Business, Belfield, Dublin 4, Ireland

    Luca Pistilli & Alessia Paccagnini

  2. Department of Management and Technology, Bocconi University, Via Roentgen 1, 20136, Milan, Italy

    Stefano Breschi & Franco Malerba

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  1. Luca Pistilli
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Appendix

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Table 10 Description of dummy variables
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Pistilli, L., Paccagnini, A., Breschi, S. et al. Gender Bias in Entrepreneurship: What is the Role of the Founders’ Entrepreneurial Background?. J Bus Ethics 187, 325–346 (2023). https://doi.org/10.1007/s10551-022-05275-y

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  • DOI: https://doi.org/10.1007/s10551-022-05275-y

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