Abstract
We document the substantial gender gap that exists among university scientists with regard to entrepreneurial activity using a variety of measures and explore factors leading to the disparity. We focus particularly on the biomedical sciences. The contextual explanation that women are under-represented in the types of positions from which faculty typically launch entrepreneurial activity is the most obvious. But the data suggest that for the biomedical sciences context is not sufficient in explaining the entrepreneurial gap. We look elsewhere to factors affecting supply and factors affecting demand. The former include gender differences in attitudes towards risk, competition, “selling” of “science,” type of research and geographic location. The latter include the role of networks, preferences of venture capitalists and “gender discounting.” We explore the associated hypotheses. We provide few tests and conclude that the research agenda is wide open and interesting.
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Notes
Bunker Whittington and Smith-Doerr (2005), study over 1,000 recipients of NIH training grants in cellular and molecular biology. They find that 30% of the male recipients patented compared to 14% of female recipients. The sample is not restricted to those employed in academe.
The authors find only 50 women listed as scientific advisors, which represents 6.4% of the 771 academic scientists identified in this role.
Family context also is important in explaining the productivity puzzle (Smith-Doerr 2004).
The existing literature, however, does not provide a conclusive answer to the question of whether a gender gap exists with regard to risk aversion. Johnson and Powell (1994), for example, provide empirical evidence regarding differences in risk propensity by gender in “managerial” and “non-managerial” populations, employing data from a random sample of 50 betting offices throughout the United Kingdom. The authors find significant differences between the “non-managerial” and “managerial” population, with the former being more risk averse. They suggest that risk propensity does not vary by gender but by ability and training. Gutter et al. (2003) use data from the 1998 Survey of Consumer Finances to examine differences in risk tolerance by gender. The authors find that women, on average, have a lower subjective risk tolerance than men, but that no gender differential exists with regard to the tolerance of objective risk. The difference between the two measures is that subjective measurements are influenced by factual information, values, opinions, and knowledge, whereas objective measurements are based on facts and observed behavior.
Among the group who performed poorly in the initial tournament, men are more likely to subsequently choose the tournament mode than women, indicating that underperforming men choose contests where they could not earn as much as they would if they were to choose the piece rate form of compensation.
Lower asks, of course, may reflect lower pay, which in turn may be related to rank and institutional setting.
Stephan et al. (forthcoming) find a strong positive relationship between the citations to SAB members’ articles and the proceeds raised at the time of the initial public offering.
Fox surveyed full-time, tenured, or tenure-track faculty in doctoral-granting departments in computer science, chemistry, electrical engineering, microbiology and physics during the period 1993–1994.
This is not to say that the Big School women stated that family constrained the amount of time they had for commercial activity. Rather, they talked about balancing their commitment to lab work, students and teaching.
Ding et al. (2006) find no gender difference in the number of co-authorship ties to scientists who have previously started or advised for-profit biomedical companies.
The line between networks and peer effects is murky. Here we think of peer effects playing a role in forming attitudes towards commercialization. Networks are seen as providing opportunities to pursue commercial activity and build commercial experience.
Fox (2003), in a survey of faculty, finds that women faculty do not have fewer male students on their teams than do male faculty, but they do have a higher number of women students and hence have, on average, larger teams. The difference between Fox’s findings and those noted in the text may relate to the fact that Fox samples across doctoral-granting institutions while Murray studies one elite institution and Chang draws his data from 14 highly rated institutions.
The report is produced by PriceWaterHouseCoopers and the National Venture Capital Association. The data is available on line at: www.pwcmoneytree.com.
The line between gender differences in “being asked” and networks is grey, as is much of this discussion, when we seek to place explanations into a framework, but it is worth noting.
In the past 20 years only 2 of the 45 Laser Prize recipients have been women; only 10% of the members of the biochemistry section of the National Academy of Sciences are women.
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Acknowledgements
The authors would like to thank Grant Black for his assistance in using the SDR data, Fiona Murray for sharing her “Big School” research with us and Bill Amis for his helpful comments. The authors would also like to thank the Kauffman Foundation for their encouragement and support.
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Use of NSF data does not imply endorsement of the research methods or conclusions contained in this paper.
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Stephan, P.E., El-Ganainy, A. The entrepreneurial puzzle: explaining the gender gap. J Technol Transfer 32, 475–487 (2007). https://doi.org/10.1007/s10961-007-9033-3
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DOI: https://doi.org/10.1007/s10961-007-9033-3