11institutetext: Santa Clara University, Santa Clara CA 95050, USA
11email<EMAIL_ADDRESS>
# Investors Embrace Gender Diversity, Not Female CEOs: The Role of Gender in
Startup Fundraising

Christopher Cassion  Yuhang Qian  Constant Bossou
Margareta Ackerman${}^{\textrm{{\char 12\relax}}}$

###### Abstract

The allocation of venture capital is one of the primary factors determining
who takes products to market, which startups succeed or fail, and as such who
gets to participate in the shaping of our collective economy. While gender
diversity contributes to startup success, most funding is allocated to male-
only entrepreneurial teams. In the wake of COVID-19, 2020 is seeing a notable
decline in funding to female and mixed-gender teams, giving raise to an urgent
need to study and correct the longstanding gender bias in startup funding
allocation.

We conduct an in-depth data analysis of over 48,000 companies on Crunchbase,
comparing funding allocation based on the gender composition of founding
teams. Detailed findings across diverse industries and geographies are
presented. Further, we construct machine learning models to predict whether
startups will reach an equity round, revealing the surprising finding that the
CEO’s gender is _the_ primary determining factor for attaining funding. Policy
implications for this pressing issue are discussed.

###### Keywords:

gender bias venture capital diversity entrepreneurship.

As gender equality continues to make strides across a wide range of industries
from STEM to medicine, there is a critical sphere where bias persists:
Compared to their male counterparts, women have little access to startup
funds, restricting them from engaging in our economy at this critical level.
According to Pitchbook, in 2019, female founders raised just 2.7% of the total
venture capital funding invested and mixed gender founding teams received
12.9% [1].

The economic impact of the COVID-19 pandemic is having severe consequences for
female entrepreneurs. Compared with 2019, the first quarter of 2020 saw a
decline in the proportion of deals made with female and mixed-gender teams and
funding allocated to female teams. In the third quarter of 2020, funding given
to female-only teams dropped to 1.8% with mixed-gender teams receiving just
11.1% [1]. There is an urgent need for understanding the nature of this
persistent bias and uncovering effective solutions for systemic change.

In the United States, only 10-15% of startups are founded by women [2]. Yet,
the number of women starting companies is not the primary issue, the far more
important problem is their lack of access to capital [3]. The funding gap
between male and female founders is particularly high at the early stage of a
venture, with an analysis of California and Massachusetts startups revealing
that female-led ventures are 63% less likely than male-led ones to obtain VC
funding [4].

While it is generally well-known in the venture community that men have an
easier time raising funds, much remains unclear. In order to ascertain
effective solutions, it is necessary to gain insight into the nature of the
problem. For instance, does having a woman on a founding team increase or
decrease fundraising outcomes? What role does the gender of the CEO play
compared to the gender of other founders? If funding is successfully raised,
how does gender impact the amount raised? How much does gender matter in
different geographic regions and across industries?

We perform an in-depth data analysis of over 48,000 companies on Crunchbase.
Our analysis suggests the presence of bias against women across geographies
and industries, which extends not only to female-only but also to mixed-gender
teams with female CEOs. We also construct machine-learning models (Decision
Tree, Random Forest, Logistic Regression, Gradient Boosted Trees, and Multi-
layer Perceptron (MLP)) to predict whether a founding team will reach a priced
funding round.111Raising a priced round is a major milestone that offers
startups the means to succeed. Our findings show the CEO’s222In startups, the
role of CEO is most often taken by one of the founders. This is nearly
ubiquitous at early stages. gender to be the most important founder
characteristic for predicting fundraising success, beating critical features
including whether the founders attended top universities and the number of
prior exits. We discuss the implications of these findings to the utilization
of machine learning models in venture capital allocation, and make
recommendations for systemic change.

##  1 Background

Gender plays a key role across the lifetime of an entrepreneurial journey:
Women are less likely to become entrepreneurs than men [5] and less likely to
get external funding once a new venture is founded [6]. The funding gap
between male and female founders is higher at the early stage of the venture
than at later stages [7]. Women are 65% less likely to get funded at early
stages and 35% less likely to be funded at later stages, when strong signals
of growth are available [4].

Consequently, women-owned businesses rely heavily on internal funding (ex.
personal finances) rather than funding from others, both debt and equity, to
finance their firms [7]. Even though the number of women-owned firms is
increasing rapidly [8], they are still left behind compared to their male
counterparts in receiving external founding.

Previous work provides valuable insight into the role of gender in the
allocation of Venture Capital funds. However, the data used in previous
studies, such as those above, tends to be geographically limited (focusing on
individual countries, often the US), or consisting of only several hundred
instances. Many questions remain to be answered: How wide is the gender gap in
the allocation of venture capital funding across geographic regions and
industries? Does gender diversity help or hinder fundraising outcomes? Does
the gender of the CEO play a special role compared to other founders?

In order gain a broader understanding into the nature and prevalence of gender
bias in VC, we perform the most comprehensive analysis to date on the impact
of gender on startup funding across geographies and industry verticals,
utilizing both statistical methods and machine learning techniques. We are
careful to account for the potential influence of the pipeline problem,
whereby fewer women seeking to engage in entrepreneurship.333The pipeline
problem is often perceived as the primary cause of the gender gap in startup
funding allocation, suggesting that the gap would be eliminated if women were
as interested as men in pursuing entrepreneurship. We devise and apply
analysis methods that shed light into these issues in a manner that cannot be
reduced to the pipeline problem. The data analysis helps inform our policy
recommendations, and we hope that it will support future research on resolving
gender bias in startup funding allocation.

##  2 Methodology

We rely on Crunchbase data to attain a data set of over 48,000 companies along
with founder information. We consider four gender compositions: founding teams
consisting entirely of male founders (male-only), founding teams consisting
entirely of female founders (female-only), teams with at least one female and
at least one male founder led by a male CEO (mixed male-led), and teams with
at least one female and at least one male founder led by a female CEO (mixed
female-led). Companies with these gender and leadership compositions are
subsequently compared, with emphasis on funding raised across a variety of
industries and geographies. We then construct machine learning models to
ascertain the importance of the team’s gender composition and the leader’s
gender in funding outcomes.

###  2.1 Data Collection

The data was obtained from Crunchbase, which prides itself for being “the
leading destination for company insights from early-stage startups to the
Fortune 1000.”444https://www.crunchbase.com/ Crunchbase provides two majors
types of data: Information on companies and data on individuals in leadership
positions. We separately retrieved both types of data as they include some
non-overlapping features. For instance, gender information is only available
as a founder attribute and is absent from the company description.

Our final dataset is an integration of the company and founder data. We first
downloaded data of 224,000 companies and 175,000 founders with attributes of
interest to our analysis. We then combined the two datasets by matching the
Company’s Website attribute in both the founders and companies dataset to
produce a new dataset of 63,462 data points. The combined dataset contains all
the attributes of the companies and all the aggregated attributes of the
founders dataset. We dropped all rows with missing values in the key
attributed (headquarter region, total funding raised, and industry) and
obtained a final dataset of 48,676 entries. 58.14% of the companies are led by
multi-member founding teams. It is worth noting that companies ranked higher
by Crunchbase tend to have fewer missing values.

While not all founders are present in the founders dataset, founders names are
present in the company dataset as comma separated attributes. Whenever a
founder’s gender is missing from the founder data set, we rely on a machine-
learning model for gender classification based on names.555We utilized the
following name-based gender classifier: https://github.com/clintval/gender-
predictor. We retrained the model, achieving an accuracy of 97.10%.

Another important aspect is identifying who is leading the startup. We define
the leader as either the CEO, or the sole founder for one person founding
teams. In order to determine leadership, we inspect the job titles of all of
the founders found on Crunchbase that have the company as their primary
organization. We set if the company is male or female led based on the gender
of the identified founder. Female-only and male-only companies are
respectively female and male led.

We reclassified the industries attribute values by reducing over one hundred
industries down to thirty by combining closely related industries, from
amongst which twenty industries with over 300 companies each were selected. We
then picked the first industry that each company provides as its primary
industry. The company’s headquarter region was used to identify its location.

###  2.2 Attribute Statistics

Before delving into extensive analysis, we share some basic statistics about
the data. As shown in Figure 1, overall founder gender distribution of the
48,676 companies in our dataset consists of 7.13% female-only companies,
80.22% male-only companies, 3.26% mixed female-led companies, and 9.39% mixed
male-led companies (see Figure 1)

Figure 1: Number of companies of each gender composition type Figure 2:
Average funding by gender composition of founding teams. Values in tens of
millions of USD.

Our analysis includes 20 industries, each consisting of at least 300 companies
(See Figure 3 for the list of industries). We omit locations with fewer than
1,500 companies, resulting in three major geographic regions, consisted of
North America, Europe, and Asia-Pacific. Since 64.01% of companies are located
in North America, we also include a detailed analysis focusing on companies
based in the top four US startup hubs: Silicon Valley Bay Area, Greater New
York Area, Greater Los Angeles Area, and Greater Boston Area. Lastly, 94.84%
of the startups in our data were founded on or after the year 2000. Please see
the Appendix for additional information about the data set.

##  3 Data Analysis

In this section, we analyse the funding allocated to founding teams with
different gender compositions. Results are reported across diverse industries
and geographic regions.

###  3.1 Analysis by industry

We begin with an analysis of funding allocation by industry across the 20 most
dominant industries identified in our data. As shown in Figure 3, there are
far more male-only companies than female-only and mixed-gender companies
across all industries. The industries Data, Commerce and Apps have the largest
number of companies while Gaming, Agriculture and Farming and Administrative
Services have the fewest. In all but 5 out the 20 industries, the next biggest
category is male-led, mixed-gender groups.

Figure 3: Number of companies for each gender composition type by industry.
All 20 industries are dominated by male-only teams. Figure 4: Total funding
allocation for founding teams with different gender composition across
industries. Values in in hundreds of billions of USD. Total funding across all
twenty one industries are dominated by companies founded by male-only founders
except in the Food industry where mixed-gender male-led teams raised more
funding than any other group type. Figure 5: Average funding allocation for
founding teams with different gender composition across industries. Values in
hundreds of millions of USD. Of the 20 industries, average funding is highest
for mixed-gender male-led teams in 11 industries and for male-only teams in 7
industries.

As shown in Figure 4, male-only and male-led mixed-gender teams receive the
great majority of funding. In particular, male-only teams receive
significantly 31 times more funding than female-only teams (statistic=11.0715,
_P_ $<0.0001$). Male-only teams also get significantly 47 times more funding
than mixed-gender female-led teams (statistic=5.8197, _P_ $<0.0001$).
Similarly, mixed-gender male-led teams also receive significantly 8.5 times
more funding than mixed-gender female-led (statistic=3.5987, _P_ $<0.0001$)
and female only teams (statistic=4.2129, _P_ $<0.0001$).

Male-only teams receive more funding in 19 of the 20 industries. In 18 of the
20 industries, there is a significant difference between the amount raised by
male-only teams compared with female-only teams, the exceptions being
agriculture & farming and biotechnology, where the difference is not
significant. On the other hand, the difference between male-only and male-led
is often insignificant, with a significant difference found in only 4 of the
20 industries.

For example, in the Data industry, male-only teams get significantly more
funding than mixed female-led teams (statistic=6.0181, _P_ $<0.0001$), and
female only teams (statistic=4.6942, _P_ $<0.0001$), and insignificantly more
than mixed male-led teams (statistic=0.6319, _P_ $=0.5276$). Similarly, in
Commerce, male-only teams get significantly more funding than mixed female-led
teams (statistic=3.4581, _P_ $=0.0005$) and female only teams
(statistic=3.7047, _P_ $=0.0002$) and significantly more than mixed male-led
teams (statistic=2.6360, _P_ $=0.0084$). Of the industries studies, Food is
the only one where male-only teams did not raise the largest amount of total
funding, however, the difference between male-only and mixed male-led teams
was not significant (statistic=0.9567, _P_ $=0.3427$).

###  3.2 Average funding by industry

The pipeline problem, the fact that fewer women engage in entrepreneurship, is
often perceived as the primary factor in the discrepancy in funding
allocation. In order to gain insight into the nature of the issue beyond the
pipeline problem, we consider the average funding allocated to teams that have
successfully raised funds, comparing the amounts raised against the gender of
the founding teams. This analysis helps gain insight while offering an
accessible demonstration of a potential gender gap to lay audiences.

As shown on Figure 5, male-only founding teams and male led founding teams
lead in average funding, receiving the highest amount of average funding
across most industries (18 out 20). In 11 of the 20 industries, mixed-gender
male-led team achieve the highest average funding, compared with 7 industries
where male-only teams raise the most average funding. In industries including
Food and Administrative Services there is a substantial gap between average
funding given to mixed male-led led teams and male-only, with the mixed teams
raising a greater amount of funding.

Of the twenty industries, there are only two industries (Energy and Education)
where female-led teams receive more average funding. Notably, there are no
industries where female-only teams raise the greatest amount of average
funding. Unlike total funding, this persistent gap in average funding to
startups that successfully raise funds cannot be explained by low numbers of
women entrepreneurs.

Comparing companies led by women, we find that in 9 of the 20 industries
female-only teams receive more average funding than mixed-gender female-led
startups.

###  3.3 Analysis by geography

Figure 6: Average funding allocation to founding teams with different gender
composition across dominant continents. Values in hundreds of millions of USD.
All continents under consideration reveal the same raking, with male-led
mixed-gender teams receiving the highest average funding, followed by male-
only teams, then female-led mixed-gender teams, and finally female-only teams
receiving the lowest amount of average funding. Figure 7: Average funding
allocation to founding teams with different gender compositions across US
startup hubs. Values in tens of millions of USD. In the US, Silicon Valley Bay
Area and New York allocate the highest average funding to male-only teams,
whereas male-led mixed-gender groups in Greater Los Angeles Area and Boston
receive more average funding than all other group types. Figure 8: Total
funding by major geographic regions. Values in hundreds of millions of USD.
Male-only founding teams receive the greater amount of funding in all regions
considered, while female-only teams and female-led teams receive the least
founding. Figure 9: Total funding by major US startup hubs. Values in hundreds
of billions of USD. Male-only founding teams receive the greater amount of
funding in all regions considered, while female-only and female-led teams
receive the least.

Considering average funding allocation, shown in Figure 6, we see the same
raking by average funding allocation across all continents, with female-only
teams receiving the lowest average funding, followed by female-led mixed-
gender teams, then male-only teams, and finally mixed-gender male-led teams
receiving the highest amount of average capital.

Analyzing startup hubs in the United States, shown in 7, we discover that in
Silicon Valley and New York male-only teams receive the highest average
funding, narrowing beating mixed-gender male-led teams. LA and Boston follow
the global trend of giving mixed-gender male-led teams the highest amounts of
average funding, followed by male-only teams. In Silicon Valley, New York, and
LA, female-only teams receive the least amount of average funding, followed by
mixed female-led teams. However, in Boston, female-only teams receive more
average funding than mixed-gender female-led teams.

In summary, companies with male CEOs receive greater funding across all
continents and US startup hubs compared with companies with female CEOs.
Mixed-gender teams perform well with respect to fundraising, often better than
male-only teams, when they are led by male-CEOs.

When comparing total funding for different gender composition teams across
continents (see Figure 8), we find that mixed-gender teams receive the great
majority of funding in the three continents considered. Europe appears to be
exhibiting the greatest preference for male-only teams, where such groups
receive significantly over 65.5 times more funding than female-only companies
(statistic=6.5061, _P_ $<0.0001$). European male-only teams raise
significantly 92.9 more money than mixed female-led teams (statistic=7.6873,
_P_ $<0.0001$). Comparing mixed gender teams, those led by men raise 15.3 more
funding than female-led groups (statistic=2.0306, _P_ $=0.0427$). Male-only
companies in Europe raise non-significantly 6 times more total funding than
mixed-gender teams led by men (statistic=0.6232, _P_ $=0.5327$).

In Asia-Pacific, male-only companies receive significantly over 37 times more
funding than female-only companies (statistic=5.1950, _P_ $<0.0001$). Male-
only teams raise insignificantly over 43 times more than mixed-gender female
led teams (statistic=1.0063, _P_ $=0.3157$). Comparing mixed gender teams,
those led by men raise insignificantly 12 times more funding than female-led
groups (statistic=1.8761, _P_ $=0.0611$). Finally, male only teams raise 3.5
time more than male-led mixed-gender teams, not statistically significant
(statistic=1.6322, _P_ $=0.1032$).

When looking at total funding for different gender composition teams across US
startup hubs (see Figure 9), similar to the continental analysis, male only
teams receive the great majority of funding for the four major hubs. Silicon
Valley exhibits some of the greatest preference for male founders, with male-
only companies receiving significantly over 27 times more funding than female-
only companies (statistic=2.9081, _P_ $<0.0038$) and significantly over 35
times more than mixed-gender female-led companies (statistic=2.7921, _P_
$<0.0054$).

In Los Angeles Area, male-only companies significantly receive over 23 times
more funding than female-only companies (statistic=3.3092, _P_ $<0.0010$).
Comparing male-only to mixed female-led companies, male-only raised
insignificantly over 30 times more (statistic=1.0888, _P_ $=0.2787$). New York
gives male-only companies over 21 times more funding than female-only
companies
(statistic=6.8847, _P_ $<0.0001$) and over 37 times more than mixed-gender
female-led companies (statistic=3.4533, _P_ $=0.0006$), both results being
significant.

Finally, analysis of Boston area shows that male-only companies receive about
16 times more funding than female-only companies (statistic=1.8629, _P_
$<0.0639$) and about 41 times more than mixed-gender female-led companies
(statistic=1.4243, _P_ $<0.1599$), however here the results were not
significant.

##  4 Predictive Models

Venture capitalists’ primary aim is to identify startups that will become
successful in the future. As such, machine learning models have been playing
an increasingly important role in the venture capital space (see, for example,
[9], [10] and [11]).666Further, many venture capital firms built their own
custom models which they do not make public in order to maintain a competitive
advantage. While predictive models can be used at any stage of investment, the
problem is particularly challenging for early stage startups, prior to the
availability of qualitative data on company performance. Prediction for later
stage startups benefit from information on factors such as revenue and growth,
making prediction significantly more accurate. On the other hand, early stage
investments, which are often pre-revenue and precede product market fit, rely
primarily on founder characteristics.

One of primary risks with the utilization of machine learning models from an
ethical perspective is the perpetuation and even amplification of existing
biases. For instance, in the context of credit markets, Black and Hispanic
borrowers are disproportionately less likely to gain from the introduction of
machine learning [12].

How much gender bias is present in startup data? To what degree does the
utilization of machine learning models stands to perpetuate, or even amplify,
gender bias in venture capital? We explore this direction by creating several
machine learning models based on founder characteristics, the dominant
characteristics available for early stage investments. We then analyze feature
importance to ascertain how much the predictions rely on the gender
composition of founding teams and the gender of the CEOs. Note that our
exploration differs significantly from prior work in predictive modeling for
startup success, since we are interested specifically in the importance of
gender for attaining a priced funding round. By contrast, most work in the
field aims to predict startup success by incorporating information about the
startup itself, including quantitative success indicators such as total
funding raised and number of employees.

###  4.1 Feature Selection

In order to ascertain investor behaviour prior to having clear success
indicators available, we focus exclusively on founder characteristics777As
mention above, while a gender gap exits at all startup stages, investors are
most reluctant to invest in women in the early stages, where female ventures
are 65% less likely to receive funding [4]. However, it is essential to avoid
including features that would be heavily altered by the target variable. For
instance, social media presence stands to alter for founders who successfully
raised funding. Similarly, information regarding investments made by the
founders is heavily influenced by their entrepreneurial success, and are as
such also omitted.

####  4.1.1 Training Features

To build the model, we first extract a set of features related to the founders
from the aggregated dataset discussed in Methodology. The following features
have been selected:

  * •

Male Led: Boolean variable that is True if the CEO or sole founder is Male,
False otherwise

  * •

Gender Composition: If the founding team is male-only, female-only, mixed
male-led, or mixed female-led

  * •

Total Previously Founded Organizations: Total number of companies previously
founded by members of the founding team

  * •

Average Previously Founded Organizations: The average number of companies
previously founded by the founders

  * •

Has Previously Founded Organizations: Boolean variable indicating True if any
of the founders previously founded an organization

  * •

Total Number of Exits: Total number of exit events in which the founders
participated

  * •

Average Number of Exits: Average number of exit events for the company
founders

  * •

Has Exits: Boolean variable indicating True if any of the founders had
previously founded a company that had an exit event

  * •

Total Number of Founders: Number of founders of the company

  * •

Multiple Founders: Boolean variable set to True if the founding team consists
of two or more founders

  * •

Same Alma Mater: Boolean variable indicating True if all of the founders went
to the same university, False otherwise

  * •

% from Top School: Percentage of founders that went to a top 100 school [13]

  * •

Top School Attended: Boolean variable set to True if any of the founders went
to a top 100 school[13]

####  4.1.2 Target Feature

The goal of these experiments is to determine if a startup reached an equity
funding round based on its founders. An equity round is when a startup sells
shares of the startup in exchange for a large investment (generally well over
a million). Equity rounds are important to the life cycle of startups largely
because they provide a significant monetary influx into the company and
represents a vote of confidence from the Venture Capital community, which
helps with subsequent rounds.

We separate the dataset into two funding stage groups, pre-equity rounds and
post-equity rounds. We define pre-equity rounds as those whose latest funding
stage is an Angel Round, Pre-Seed, Seed Round, or Convertible Note. We define
post-equity rounds as those that whose latest funding stage is Series A,
Series B or beyond, or Corporate Rounds. Using that, we construct models to
predict whether a founding team has reached a priced round.

###  4.2 Model Analysis

Using hyperparameter grid-search to obtain the best model of each type, we
constructed the following models: (1) Decision Tree (DT), (2) Random Forest,
(3) Logistic Regression (LR), (4) Gradient Boosted Trees (GBT), and (5) Multi-
layer Perceptron (MLP), for each of the worldwide and US data. MLP had the
highest accuracy on worldwide data, at $63.73\%$. Similar results were found
for US data with MLP giving the highest accuracy of $63.61\%$. Figure 1 and
Figure 4 summarize the results for worldwide and US data, respectively. All
models performed comparably, with worldwide accuracies varying by only 0.86%.

Model | AUC | Precision | Recall | Accuracy
---|---|---|---|---
Decision Tree | 53.30 | 59.90 | 53.23 | 62.91
Random Forest | 53.20 | 59.78 | 53.17 | 62.86
Logistic Regression | 52.70 | 61.38 | 52.73 | 63.00
Gradient Boosted Trees | 53.00 | 60.04 | 53.04 | 62.88
Multi-Layer Percepton | 53.80 | 63.92 | 53.80 | 63.72
Table 1: Predictive model results for worldwide data.

Early stage predictions are known to be highly challenging. It is essential to
emphasize that no information about the companies has been provided beyond
founder features, in order to ascertain the impact of gender on early stage
investing. It is unlikely that much higher accuracy is possible without
incorporating features beyond the scope of founder characteristics.

Model | AUC | Precision | Recall | Accuracy
---|---|---|---|---
Decision Tree | 54.20 | 58.36 | 54.17 | 60.68
Random Forest | 54.70 | 58.90 | 54.70 | 61.00
Logistic Regression | 55.80 | 60.97 | 55.83 | 62.08
Gradient Boosted Trees | 55.00 | 60.15 | 55.00 | 61.52
Multi-Layer Percepton | 57.60 | 63.63 | 57.59 | 63.61
Table 2: Predictive model results for the US dataset.

####  4.2.1 Feature importance in tree based models

Considering feature importance enables us to ascertain how significant are
gender-related characteristics compared with other founder attributes, such as
prior exits or whether founders attended top schools. Table 4 and Table 3
detail the feature importance for the Decision Tree and Random Forest
models.888We report feature importance for the interpretable tree-based
models, emphasizing that all models obtained comparable accuracy. Importance
analysis for other models are left for future work. The results show that
whether a company is led by a male CEO is by far the most important feature in
the decision tree model, and also the top feature in the random forest model
for both the worldwide and US-only datasets.

Feature | Decision Tree | Feature | Random Forest
---|---|---|---
Male Led | 25.62$\%$ | Male Led | 15.50$\%$
% from top school | 18.81$\%$ | Total Number of Founders | 12.97$\%$
Has Exits | 18.81$\%$ | % from top school | 11.26$\%$
Same Alma Mater | 10.10$\%$ | Same Alma Mater | 9.12$\%$
Total Number of Founders | 6.99$\%$ | Has Exits | 7.86$\%$
Table 3: Decision Tree and Random Forest feature importance for top 5 features for worldwide data. Whether the company is led by a male CEO is the most important feature for both the decision tree and random forest models, more important than the features addressing the number of prior exits and founders’ alma mater.  Feature | Decision Tree | Feature | Random Forest
---|---|---|---
Male Led | 19.34$\%$ | Male Led | 18.40$\%$
Number of Exits | 19.29$\%$ | Number of Founders | 13.78$\%$
% from top school | 14.81$\%$ | % from top school | 9.38$\%$
Same Alma Mater | 12.46$\%$ | Same Alma Mater | 7.91$\%$
Number of Founders | 8.22$\%$ | Avg Number of Exits | 7.21$\%$
Table 4: Predictive model feature importance on US data. According to both the
decision tree and random forest models, the most important feature for
reaching a priced round in the US is whether the company consists of only male
founders.

For the Gradient Boosted Tree (GBT) model on US data, the top 5 features are
ranked as follows: whether the founding team is male-only (14.46$\%$), number
of founders (13.04$\%$), percent of founders from top schools (12.03$\%$),
number of exits (11.46$\%$), and if all the founders have graduated from the
same university (10.07$\%$). For the worldwide GBT model, where GBT achieved
the second lowest accuracy of the models created, the top feature is the
number of founders.

In summary, feature importance analysis for the tree-based models indicates
that in most instances, for both worldwide and US-only datasets, gender is key
to fundraising success. We find that the main indicator of whether a startup
will reach a priced funding round centers on gender, either the gender of the
CEO or whether the team consists entirely of male founders.

##  5 Conclusions and Recommendations

Our analysis suggests the presence of a pervasive and substantial bias against
female-led startups across geographies and industries. Looking at average
funding, where only startups that have raised funds are considered, lets us
eliminate the pipeline problem as a a primary explanation for discrepancies in
funding allocation. The analysis reveals that, across all but 2 of the 20
industries considered, male-led teams received the highest average funding.
Across all three continents in our analysis, North America, Europe, and Asia-
Pacific, the highest average funding went to mixed-gender teams led by male
CEOs. In all three continents, the least average funding went to female-only
teams, followed by female-led mixed-gender teams.

Among US startup hubs, Silicon Valley and New York gave the highest average
funding to male-only teams, while LA and Boston gave greatest average support
to mixed-gender teams led by male CEOs. As in the continental analysis,
female-only teams receive the lowest average funding, following by mixed-
gender teams with female CEOS.

Our ML-based analysis reveals gender characteristics to be of highest
importance amongst founder features for reaching a prices round, in
particular, more important that traditionally prized characteristics
pertaining to whether the founders have attended top universities or had prior
exits. Worldwide analysis reveals the gender of the CEO to the most important
feature. On US data, machine learning modelling shows that the most important
characteristic tends to be whether all founders are male.

In summary, we find that across all geographic regions and the great majority
of industries, companies a male CEO have much better funding outcomes those
with female CEO. The gender of the CEO appears to be _the most_ important
factor in fundraising. With no exceptions across geographies and industries,
our results show that startups led by male CEOs raise more money than startups
raised by female CEOs, irrespective of the gender of the rest of the founding
team. On the other hand, having women as founders but not CEOs improves
funding results in some (but not all) cases, sometimes by a substantial
margin. Across all geographies (but not all industries), female-led companies
achieve better funding outcomes when they include a male co-founder.

###  5.1 Implications for machine learning modeling for investment decisions

Our machine-learning analysis reveals that CEO’s gender to be the most
important amongst founder characteristics for attaining a priced funding
round. In particular, gender composition was found to be more important than
characteristics that are known to be prized by venture capitalists, such as
the number of prior exits or the founder’s alma mater. This surprising finding
not only reveals the primary role of gender in venture capital allocation, but
also warns of potential pitfalls when applying machine learning models to
investment decisions.

Machine learning models in other spheres, such as credit markets, have already
been shown to inflate biases [12]. We recommend exercising caution when
building machine learning models for startup success prediction, in order to
reduce the impact of gender on the resultant decision making. Most
importantly, features directly capturing the gender of the founders should be
omitted.999Gender information has been incorporated into previous ML models
for startup success, see, for example [11].

Further, we observe that features such as prior exits, while not directly
capturing bias, may play an important role in perpetuating it. With
longstanding low access to startup funding, women have much lower chances of
having had previous exits.

###  5.2 Discussion and policy implications

One of the most important findings of this analysis is the critical role of a
CEO’s gender for fundraising outcomes, even in mixed-gender teams. This is
notable because in startups, particularly at early stages, division of labour
amongst founder is a less clear cut than in mature companies. Thus, it
unlikely that a mixed-gender company’s performances in terms of investor
returns will be impacted on the basis of whether a male or female co-founder
is designated as the CEO.

Yet, fundraising is often handled by the CEO, making the founder identified as
such the primary link between the startup and any potential investors. Even
when other founders are present, the CEO is expected to lead the discussion on
behalf of their startup. Consequently, any bias against women, implicit or
otherwise, is likely to manifest most strongly if the CEO is female.

The critical role of the CEO in funding outcomes may thus be reduced to
differences in how investors treat men and women. Prior research points to
desperate treatment of men and women during startup pitches. In a study on
interactions at TechCrunch Disrupt in New York City, investors asked men to
expand on how the plan to reach success, whereas women were asked to defend
themselves against failure [14], which hindered women’s ability to raise
funds. Notably, both male and female investors exhibited this bias against
female founders.101010There is a prevalent notion that the key to eradicating
gender bias in startup investing lies in increasing the number of female
investors. This view is oversimplified and potentially misleading. Both men
and women are highly prone to bias against women [15]. While increasing gender
diversity amongst investors is important for a variety of reasons, tackling
gender bias against female founders calls for more comprehensive solutions.
Further research is needed to elucidate the impact of implicit gender bias on
fundraising outcomes and uncover the reasons behind ubiquitous lower
propensity towards investing in female CEOs across the globe.

Our findings show that across all continents considered and some US startups
hub mixed-gender are given higher average funding, when they are male-led.
This likely stems from the inherent advantage of mixed gender teams. Gender
balanced teams perform better than male-dominated teams in terms of sales and
profits [16], and gender diverse executives teams are 21% more likely to yield
higher financial returns [17]. The venture capital firm First Round reported
that their investments in companies with at least one female founder were
meaningfully outperforming their investments in all-male teams [18]. In fact,
their investment in companies with a female founder performed $63\%$ better
than their investments with all-male founding teams [18].

The benefit of a gender diversity helps mixed-gender teams raise funding, but
only when they are led by a male CEO. The consistently lower funding
allocation, both in total and on average, to mixed-gender teams when they are
led by women CEOs points to the severity of gender bias in startup capital
allocation.

In recent years, a number of Venture Capital firms emerged with the mandate to
invest in teams with at least one female founder. However, these investment
firms tend to be (1) late stage, and (2) utilize a “follow” investment
strategy, investing only after another VC firm makes a substantial investment
and sets the deal terms. This does little to help increase the number of
female founders.

Our findings suggest that importance of investing in female-led companies to
correct the gender bias in capital allocation. We recommend the formation of
venture capital firms with the mandate to invest in companies with female
CEOs. Investing in women-led, mixed-gender teams should allow investors to
benefit from the performance boost of gender diversity, while helping to
correct the long standing bias against female business leaders. Investors with
expertise to lead early stage deals applying such practiced can further reap
the benefit of early investing, receiving large equity in promising deals.

## References

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##  6 Appendix

This appendix includes additional information on the data used in our
analysis.

Table 5: Total Number and Percentage of Companies per Region Geography | # of companies | Percentage
---|---|---
North America | 30,212 | 62.07%
Europe | 8,932 | 18.35%
Asia-Pacific | 7,968 | 16.37%
Latin America | 1,308 | 2.69%
Gulf Cooperation Council | 256 | 0.53%
Total | 48,676 | 100.0%
Table 6: Total Number and Percentage of Companies per Region Geography | # of companies | Percentage
---|---|---
Silicon Valley Bay Area | 7,611 | 46.98%
Greater New York Area | 4,420 | 27.28%
Greater Los Angeles Area | 2,416 | 14.91%
Greater Boston | 1,754 | 10.83%
Total | 16,201 | 100.0%
Table 7: Total and Percentage of Companies per Industry Verticals | # of companies | Percentage
---|---|---
Data | 6,200 | 12.74%
Commerce | 5,008 | 10.29%
Apps | 4,789 | 9.84%
Finance | 3,679 | 7.56%
Information Technology | 2,822 | 5.80%
Health Care | 2,755 | 5.66%
Biotechnology | 2,663 | 5.47%
Advertising | 2,442 | 5.02%
Consumer Electronics | 2,236 | 4.59%
Hardware | 1,826 | 3.75%
Education | 1,690 | 3.47%
Content Design | 1,670 | 3.43%
Internet | 1,575 | 3.24%
Community and Lifestyle | 1,388 | 2.85%
Clothing and Apparel | 1,107 | 2.27%
Energy | 884 | 1.82%
Food | 828 | 1.70%
Administrative Services | 729 | 1.50%
Gaming | 613 | 1.26%
Agriculture and Farming | 612 | 1.26%
Others | 3,160 | 6.49%
Total | 48,676 | 100.0%