The U.S. venture capital industry, and the high-tech startups supported by it, have a well-known gender gap. In 2017, 16 percent of the nearly $83 billion invested in U.S. venture-backed startups went to companies with at least one female founder, and just 2.5 percent went to startups with all-female founders. Meanwhile, an estimated 9 percent of general partners (the people making investment decisions) at leading U.S. venture capital firms are women.
Women’s underrepresentation among leading venture capital firms and venture-backed startups is especially stark when compared with their rates of participation in the workforce (47 percent), business ownership (36 percent), high-tech industry employment (30 percent), or as alumni of the feeder institutions (universities, degree programs, corporations) that tend to populate the sector (various percentages). This suggests that particular barriers exist for women in entrepreneurship beyond those already faced in related fields.
This report adds to the relatively limited research in this area by studying patterns of women-founded, venture-backed startups in the United States between 2005 and 2017. While others have tended to look at topline aggregates of venture deals and funding amounts by the gender composition of founding teams, we focus on the number of new companies entering the venture-backed pipeline each year by tracking “first financings” (initial venture investments).
We take this approach for two reasons. First, as with our previous work, we want to understand the flow of new companies entering the venture-backed universe each year—those closest to “starting up.” Second, we want to study the relative performance of companies over time. To do that, we must group them into cohorts along a common dimension—in this case, companies that raise a first round of venture capital during the same year.
Thirteen first-financing cohorts were produced for the years 2005 through 2017. Startups with at least one identifiable female founder are considered “women-founded.” All other companies are labeled “non-women-founded.” The number of first financings by founder gender group are examined over time, by industry, and across U.S. metropolitan areas. We also compare follow-on outcomes for women-founded companies versus non-women-founded ones—such as the percentage that raise follow-on rounds of capital or reach an “exit” (initial public offering or acquisition).
Women-founded companies represented a small share of venture capital first financings between 2005 and 2017, accounting for just 16 percent of such activity over the period. However, they also showed remarkable improvement over time, rising from just 7 percent of first financings in 2005 to 21 percent in 2017—expanding the share of total activity accounted for by women-founded companies in all but one year.
Once funded, the percentage of women-founded startups that raised additional rounds of capital was similar to non-women-founded firms. Fifty-two percent of women-founded startups raised a second round of capital within three years of a first financing and 37 percent raised a third round within five years. Those same figures for non-women-founded companies were 52 percent and 36 percent.
Rates of initial public offering for venture-backed startups ten years after a first financing were about the same for women-founded and non-women-founded startups (3.8 percent versus 3.7 percent). For acquisitions (including buyouts), ten-year exit rates for women-founded startups were slightly lower than for non-women-founded companies (34 percent versus 38 percent).
Women-founded companies exist in nearly every industry in our venture capital database, but are concentrated in consumer goods and services and in healthcare. The software industry produces the largest number of women-founded startups, accounting for 40 percent of women-founded companies. But, this is still slightly lower than the software industry’s share of all venture-backed startups (44 percent).
Women-founded startups are concentrated in America’s leading startup communities, including in San Francisco, New York, Boston, and Los Angeles. The San Jose metro area (Silicon Valley) is the lone exception among the leaders, where the women-founded share is below average. Other cities with persistently high rates of women-founded startups include Ann Arbor, Memphis, Philadelphia, Pittsburgh, Boulder, and Washington, D.C.
These findings, considered jointly with other research, suggest a need for greater representation of women-founded companies in venture funding markets, or more to the point, for investors to more adequately capitalize these high-potential entrepreneurs. As the data show, the percentage of women-founded startups that reach key performance milestones (follow-on financings, IPOs) is similar to non-women-founded firms. The lone exception is the acquisition rate, which is lower for women-founded startups. We don’t know yet what’s causing this disparity, but existing research suggests gender biases and a lack of gender diversity among investors may be partly to blame.
This analysis also points to a need for further research in a few key areas. For example, a lack of female venture capitalists has been identified as a likely contributor to the women’s funding gap, and social and cultural factors appear to explain some of the geographic variation in women’s entrepreneurship. But, less has been established that explains why women-founded startups are less prevalent in some industries versus others or why they are less likely to be acquired. Finally, a robust analysis of differences in exit values or investment returns of venture-backed companies by founder gender is needed, but availability of the requisite data has been an obstacle.
Overall, a great deal of progress has been made in recent years for women founders at the early stage of the funding market, but there remains plenty of room for improvement for women in venture-capital-funded businesses overall, in the information technology and enterprise services sectors, in a number of startup communities, and among venture capitalists themselves. While there is more yet to learn, the currently available evidence suggests this last point—diversifying the investor base—would be an effective way to improve conditions immediately. Women’s entrepreneurship education, mentorship, and peer and other support programs could also help. Gendered processes at home and the workplace more broadly will also determine whether more women choose to pursue hyper-growth entrepreneurship to begin with.
Ian Hathaway is Research Director at the Center for American Entrepreneurship, where he leads content development efforts, publishes research and commentary, and advises on policy and strategy.
The primary data in this study come from PitchBook, a leading vendor of information on venture capital deals, and the individuals, companies, and investors involved in them. All figures here include venture deals that were completed in each particular calendar year between 2005 and 2017 (inclusive) for companies with headquarters in the United States. Deals completed among the “pre-venture” series (accelerator, incubator, angel, or crowdfunding) are excluded (because they are not included in round sequences in the PitchBook database). More than 95 percent of first financing deals are either Seed or Series A.
Central to the work here, PitchBook tracks information on individual founders for companies in the database, and assigns a gender identifier through a two-step process—first manually via a primary research and secondly through an algorithm that assigns gender based on given names. Where results differ, further research is conducted to resolve discrepancies. Individuals are given gender values of female, male, or other/unknown. As a simple check, we conducted a detailed review of a random sampling of companies identified as having at least one female founder by PitchBook and found their results to be entirely accurate.
Companies in this study are considered “women-founded” if they had at least one verified female founder—as opposed to cases where all founders were female. The former was chosen over the latter for a few reasons. First, the size of founding teams varies widely across industries. In life sciences, for example, the number of founders can be quite large, and imposing an all-founders requirement for gender would skew the results. An all-founders requirement would also classify companies with founder-level contributions by women as “non-women-founded,” which not only feels outside the bounds of what we’re trying to understand here, it is arguably inaccurate. Third, imposing an all-women requirement for companies to be women-founded goes precisely against the entire point of promoting diversity in entrepreneurship. Finally, an all-founders requirement would limit our analyses because the pool of companies would be so small.
While many reports on venture capital deals and on women-founded startup funding focus on the entirety of venture activity (deals or capital invested at all stages), this study focuses primarily on the first round of financing by professional investors (“first financings”) for a few reasons. First, we are primarily interested in understanding the venture-backed companies most closely associated with “starting up” (as opposed to “scaling up”), and we do this by capturing the companies as they enter the venture pipeline (a measure of flow). Second, we wanted to better understand the number of companies that get funded, rather than the amount of capital going into them. Finally, since we want to understand how companies are performing over time in a comparable way, we had to construct annual cohorts and observe key outcomes over a similar time horizon.
To produce annual “first financing” cohorts of companies by gender identity of founding teams, a multistep approach was taken. First, we used the PitchBook platform to tabulate annual lists of companies that completed a first round of venture financing in a calendar year for each of the thirteen years. Next, the lists were sent to PitchBook, which used the back end of its database to flag the companies where at least one female founder could be verified. The list was returned to us with indicators for companies with at least one female founder. We then constructed two corresponding lists for each annual cohort of first financings back on the PitchBook platform—one for companies where at least one female founder could be verified and one where at least one female founder could not be identified. That allowed us to conduct most of the remaining analyses contained in the report (the lone example was geography; see below).
The tabulations and plotting of data across all first financings, by industry, and by geography, were relatively straightforward. The number of first financings in a particular year, naturally, were those completed between January and December. Sector and detailed industry classifications are pre-populated by PitchBook. For geography, PitchBook provided us with lists of first-financing counts by state, city, and zip code. Using files from the U.S. Office of Management and Budget and the Census Bureau, each combination (where the information was available) was mapped back to any one of a metropolitan area, micropolitan area, neither of these, or unknown. This analysis was restricted to metropolitan areas and the United States as a whole.
The analyses for follow-on outcomes were conducted in the following way. Each first-financing cohort (say, 2005 for women-founded companies) was loaded into the PitchBook platform, and search queries were performed based on outcome (e.g., second round of financing, IPO) and the appropriate time lag (three, five, eight, or ten years from first financing). For example, an outcome of acquisition for a company in the 2006 cohort would have had to occur after its first financing in 2006 and before either December 31, 2014 or December 31, 2016 (eight-year and ten-year exits), and so on. Numbers were tabulated for each outcome for the maximum number of cohorts and presented as a share of all first financings for each cohort. All analyses were conducted in the PitchBook platform based on the cohort lists derived as per the above.
Finally, because we took a conservative approach for identifying companies as women-founded and non-women-founded (i.e., those where a female founder could not be confirmed), the latter category may be considered by some as overly expansive, since a number of these companies lacked information on founders entirely. As a check, we replicated our analysis across three groups of founder types: women-founded (at least one verified woman founder), non-women-founded (at least one verified male founder and no women founders), and unknown (where the gender or identity of no founders could be confirmed). The results were strikingly similar.
.png)
.png)
.png)
.png)
.png)