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VC Ecosystems
Analysis of key indicators of US VC ecosystem development
Credits & Contact
Analysts
JOELLE SOSTHEIM Analyst
joelle.sostheim@pitchbook.com
DARREN KLEES Data Analyst
darren.klees@pitchbook.com
Contact PitchBook
RESEARCH
reports@pitchbook.com
Contents
Key takeaways
1
Introduction
2
Approach
2-3
Density
3-4
Resources
4-5
Talent
5-6
Case study: California,
Illinois and Florida
7-8
Discussion of data
8-9
Table 1: Density
10
Table 2: Resources
11
Table 3: Talent
12
Published on June 26, 2018
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judgment.
To supplement PitchBook analysis on venture investment activity, this report
introduces a framework for deeper evaluation of venture ecosystems in the
United States. Building extensively on existing industry research, we present
three key indicators of ecosystem development: density, resources and talent.
While this report is just a starting point for comparing factors of development,
we hope to address the lack of data and research in private markets by providing
readers a new lens with which to understand venture ecosystems.

Key takeaways
Few states have mature late-stage ecosystems. Many smaller
ecosystems have a healthy density of early-stage startups but
lack a concentration of late-stage companies. While states
with low late-stage density also have fewer exits, healthy
early- and very early-stage vitals could indicate potential for
growth.
Proximity to capital is expensive. States with the most access
to local capital ($2.5 million+ per venture-backed startup) also
have higher costs of labor and housing relative to the national
average. States with slightly less capital per startup (around
$1 million per venture-backed startup) have costs closer to the
mean, while regions with low values of local venture capital
also have the lowest costs.
Entrepreneurial experience is relatively equal across states,
but talent clusters. Regarding local talent, the proportion of
startup founders who previously founded another company
hovers around 3%-5% in most states, indicating relative
equality in entrepreneurial experience. Local talent retention
is more variable, however. The proportion of startups with
founders who studied at a four-year institution in-state
appears to be strongest in regions with top-tier colleges and
universities.
2
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Introduction
The factors most critical to a startup ecosystem's development
have long been subject to discussion and debate. In "Regional
Advantage," AnnaLee Saxenian asserts that cultural influences
such as intellectual openness and collaboration played a key role
in the divergence of development between Silicon Valley and
Boston.1 Further, in "Startup Communities," Brad Feld discusses
entrepreneurial density, clusters of talent and leadership as crucial
inputs in the development of entrepreneurial ecosystems.2 Factors
of development are important not just to investors, but also to
local governments and business communities striving for a healthy
business environment.
Beyond factors of ecosystem development, another point of
debate is whether startups in well-developed venture ecosystems
generate more attractive returns for investors. Some investors and
entrepreneurs have argued that startups with more limited access
to resources may be more capital efficient and exhibit lower
burn rates. Critics assert that the lack of intellectual diversity
in saturated ecosystems breeds entrepreneurs that address
only problems with which they are familiar, overlooking market
opportunities. To address these shortcomings, select VCs (e.g.
Rise of the Rest, Elsewhere Partners) have adopted strategies that
aim to source deals outside of the coastal venture hubs.
In this report, we investigate several factors that we believe
to be indicators of venture ecosystem development, building
extensively upon existing industry research.
Approach
Our approach builds on the entrepreneurial ecosystem framework
developed by the Ewing Marion Kauffman Foundation.3 This work
asserts that the development of an entrepreneurial ecosystem
is a product of density, fluidity, connectivity and diversity. While
the Kauffman Foundation studies many types of entrepreneurship
(Main Street, small business, etc.),4 we focus specifically on high-
growth, venture-backed startups and transform the structure
of their ideas to more acutely measure venture ecosystems. To
measure quantitative and qualitative factors in startup ecosystem
development, we leverage PitchBook metrics and administrative
data to analyze three indicators: density, resources and talent.
1: "Regional Advantage: Culture and Competition in Silicon Valley and Route 128," AnnaLee
Saxenian, 1994
2: "Startup Communities: Building an Entrepreneurial Ecosystem in Your City," Brad Feld, 2012
3: "Measuring an Entrepreneurial Ecosystem," Kauffman Foundation Research Series on City,
Metro, and Regional Entrepreneurship, Dane Stangler & Jordan Bell-Masterson, March 2015
4: "2017 Kauffman Index of Startup Activity: Metropolitan Area and City Trends," The
Kauffmann Index, Arnobio Morelix, Robert Fairlie & Inara Tareque, May 2017
3
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
The purpose of this research is to examine profiles of venture
ecosystems by state, as measured by the variables below. In doing
so, we aim to identify characteristics of ecosystems at different
stages of development, using California, Illinois and Florida as case
studies. Private markets generally lack transparency to identify
cause and effect. While this report is just a starting point for
comparing ecosystem factors to exit success, we hope to address
the lack of data and research in private markets by providing
readers a new lens to understand underlying factors that may
impact ecosystem development and, subsequently, returns.
Indicator 1: Density
The term "entrepreneurial density" generally refers to the
concentration of entrepreneurs (and related employees and
students) in a geographic region, per population.5 Researchers
from the Kauffman Foundation note that measuring business
concentration per population is helpful to compare "relative
density of entrepreneurship" across regions, rather than volume
of deals or capital invested alone.6 Because we are interested in
venture ecosystems, we measure density by assessing the number
of businesses that have received venture funding by stage of
funding. Isolating startup density throughout the venture lifecycle
(very early, early and late stages) helps to illuminate the relative
concentration of companies in an ecosystem.

These variables are standardized by dividing by state population
(in millions) for comparison across regions. We view very early-
stage (angel, seed, accelerator and incubator-stage companies)
density as a leading indicator, because it is representative of new
venture-backed startups in an ecosystem, which contribute to
density in the long run should they survive.
5: "Entrepreneurial Density," Feld Thoughts, Brad Feld, August 23, 2010
6: "Measuring an Entrepreneurial Ecosystem," Kauffman Foundation Research Series on City,
Metro, and Regional Entrepreneurship, Dane Stangler & Jordan Bell-Masterson, March 2015
Very early-stage density =
# of companies that received angel, seed, acc, inc financing in year t
state population (millions)
Early-stage density =
# of companies that received early-stage VC financing in year t
state population (millions)
Late-stage density =
# of companies that received late-stage VC financing in year t
state population (millions)
Indicator 1: Density
Indicator 2: Resources
Indicator 3: Talent
Links to tables:
4
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Developed venture ecosystems are known to have healthy deal
activity across stages. That is, if a startup receives early-stage
funding, they can also find late-stage funding to grow, scale and
eventually exit. We find that most ecosystems have relatively
healthy density of very early-stage companies (we provide further
detail later on in this note on how the interaction of variables can
provide more color here). Late-stage density, however, appears
to be high only in well-developed ecosystems, like California
and Massachusetts. While states with low late-stage density also
have fewer exits, healthy early- and very early-stage vitals could
indicate potential for growth.
Indicator 2: Resources
The second indicator, resources, is intended to measure local capital
availability to venture-backed startups, participation of outside
investors in an ecosystem and the relative cost of doing business.
We assert that the first variable, local VC per venture-backed startup,
is a lagging indicator of venture ecosystem development. Investors
tend to cluster in areas with perceived investment opportunities
to gain advantages in deal sourcing and portfolio company
management, so we would expect greater availability of local capital
to follow the presence of investable startups. We note that less-
developed ecosystems score low here. However, if one observes
this factor in conjunction with a low late-stage and high early-stage
density score, this could suggest opportunity for investors to provide
undercapitalized ecosystems with growth funding.
Local capital per venture-
backed startup
=
dry powder held by state VC firms
# of state VC -backed startups
Outside VC participation =
# of state deals led by outside investor
# of all state deals
Cost of labor (z-score)7
=
median state tech salary - average of all median tech salaries
standard deviation of all median tech salaries
Cost of housing (z-score)8
=
median monthly state housing cost - average of all median housing costs
standard deviation of all median housing costs
7: We use computer programmer salary to represent tech salary, per data from the US Bureau of
Labor Statistics. The second term is the average of all state median values, and the third term is the
standard deviation of all state median values.
8: Due to a lack of availability of standardized office rent data by state, we use estimated median
monthly housing cost to represent housing market costs by state. Data is sourced from the US
Census Bureau, using 2016 figures. The second term in the equation is the average of all state
median values, and the third term is the standard deviation of all state median values.
Indicator 1: Density
Indicator 2: Resources
Indicator 3: Talent
Links to tables:
5
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Conversely, we perceive outside VC participationthe proportion
of all ecosystem deals led by outside investorsto be a leading
indicator of development. In the absence of local capital, deals
led by outside investors could indicate ability to attract capital
and larger pools of funding. On the other hand, a prolonged
dependence on outside capital could hinder an ecosystem's ability
to establish local resources.
Salary and housing costs are included as measurements of
primary costs to startups. Given the low capital intensity of lean
tech startups, we use these as starting points to measure costs
that pull on startup resources. To compare these measures across
ecosystems, we normalize each measure of cost by creating
z-scores, using average and standard deviation of each sample's
medians. A higher z-score indicates a greater deviation of salary
or housing costs from the mean.
Indicator 3: Talent

Maryann Feldman states that "innovation depends on knowledge,"
noting that scientific and technical expertise are necessary for
business and product advancements.9 Innovation also tends to
develop within clusters, which makes the knowledge and talent
found in universities an important source of both ideas and
employees/founders. We use the percentage of state population
that is enrolled in four-year higher education institutions to
indicate the relative supply of educated individuals available
to startups (as employees, collaborators or future founders).
To assess the retention rate of local talent, we measure the
percentage of founders in an ecosystem that have a degree from
a local university.10
Next, we posit that founder experience is a lagging indicator of
ecosystem development (because it is dependent on previously
established businesses), but believe it is informative of the
quality and prospective success of businesses in the region. More
seasoned entrepreneurs can contribute to success thanks to their
experience operating a business, expertise in their chosen sector
and developed network. In most states, this metric hovers around
3%-5%, indicating a relative equality of proportionate experience
across ecosystems.
Finally, we assert that diversity contributes to business formation
and financial success of startups in an ecosystem. We also note
that these metrics tend to be more static and provide qualitative
9: "An Examination of the Geography of Innovation," Maryann Feldman, 1993
10: We note that information regarding the university or college attended by founder(s) is
known for only 59.5% of the dataset. The unknown data for the remaining portion of the
sample may create bias in the local talent retention variable.
Indicator 1: Density
Indicator 2: Resources
Indicator 3: Talent
Links to tables:
6
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
information on individuals in an ecosystem. Research aggregated
by the Kauffman Foundation suggests that immigrants have a
high propensity for entrepreneurship,11 and the National Venture
Capital Association states that as of September 2017, 51% of US
unicorns were founded by immigrants.12 Further, research from
the Peterson Institute for International Economics suggests
that including women in a company's C-suite may improve
financial performance.13 Last, McKinsey & Company finds that
companies with high racial and ethnic diversity are 35% more
likely to financially outperform industry peers.14 Accordingly,
we measure diversity via immigrant population in a state, as
well as the proportion of regional founders that are female.
Because PitchBook does not measure racial or ethnic diversity
in its proprietary database, we use a proxy to represent the
share of business owners in a region that are racial and/or ethnic
minorities. A higher score on all accounts indicates greater
diversity within an ecosystem's entrepreneurs or population.
11: "Immigration," State of the Field, Sari Kerr, January 10, 2018.
12: "We're Suing the Government over Immigration. Here's Why," NVCA Blog, Bobby Franklin,
September 20, 2017
13:"Is Gender Diversity Profitable? Evidence from a Global Survey," Peterson Institute for
International Economics, Marcus Noland, Tyler Moran & Barbara Kotschwar, February 2016
14: "Why Diversity Matters," McKinsey & Company, Vivian Hunt, Dennis Layton & Sara Prince,
January 2015
15: We use 2016 figures of student population, as 2017 data is not yet available. 2016 state
population figures are also used in this calculation for uniformity.
16: This figure is calculated with data from the Annual Survey of Entrepreneurship, data last
available from 2015.
17: 2016 figures are also used for immigrant population, as 2017 data is not yet available. 2016
state population figures are also used in this calculation.
Local talent retention =
# of startup founders with degree from universities in state
# of all startup founders in state
Student population15 =
# of students enrolled in 4-year higher education institutions in state
state population
Entrepreneurial experience =
# of repeat founders in state
# of founders in state
Gender representation =
# of female founders in state
# of founders in state
Minority business owners (proxy)16 =
# of minority business owners in state
# of business owners in state
Immigrant population17 =
state immigrant population
state population
Indicator 1: Density
Indicator 2: Resources
Indicator 3: Talent
Links to tables:
7
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
State
Illinois
Angel/seed/acc/inc
density
13
Early-stage density
6
Late-stage density
4
Local capital per
funded startup
$1,482,211
Outside capital
30%
Cost of labor (z-score)
0.71
Cost of housing
(z-score)
0.23
Entrepreneurial
experience
4%
Local talent
30%
Student population
5%
Minority business
ownership (proxy)
17%
Gender representation
12%
Immigrant population
14%
State
California
Angel/seed/acc/inc
density
53
Early-stage density
29
Late-stage density
16
Local capital per
funded startup
$5,479,920
Outside capital
21%
Cost of labor (z-score)
1.34
Cost of housing
(z-score)
1.95
Entrepreneurial
experience
5%
Local talent
26%
Student population
4%
Minority business
ownership (proxy)
34%
Gender representation
12%
Immigrant population
27%
State
Florida
Angel/seed/acc/inc
density
10
Early-stage density
3
Late-stage density
1
Local capital per
funded startup
$285,806
Outside capital
21%
Cost of labor (z-score)
-0.16
Cost of housing
(z-score)
0.08
Entrepreneurial
experience
4%
Local talent
16%
Student population
7%
Minority business
ownership (proxy)
25%
Gender representation
11%
Immigrant population
21%
Case study: California, Illinois and Florida
To illustrate an evaluation of these indicators, we present a
comparison of ecosystems in varying stages of development.
California is a good example of a well-developed startup
ecosystem because it harbors the mecca of startup hubs: Silicon
Valley. These data observations suggest that startup density
per capita is high across all stages of maturity in California,
particularly late stage. California also has a higher historical exit
count than any other state, thanks to the high supply of late-
stage companies in the region. We note that despite having a
healthy amount of local capital per venture-backed startups, the
relative costs of labor and housing are more expensive. These
high costs help to explain in part the available capital, though
the concentration of global venture firms and large funds also
contribute to this statistic. Finally, minority and immigrant
representation are also strong in the region. Though these may
not have a direct correlation to startup success, they indicate a
more diverse state-wide populationthat is, local individuals who
may be inclined to work for or found a startup. How much this
diversity is actually represented in Silicon Valley has been subject
to criticism, however, so we take this consideration in tandem with
these data observations.
8
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Next, we use Illinois as an example of a less-developed but still
active ecosystem, given that venture firms in-state consistently
raised nine funds in each of the years between 2015 and 2017
(high values, historically), and capital invested in Illinois startups
grew 62% from 2016 to 2017. Density is much lower than
California's across all stages, particularly in the late stage, which
may be indicative of a lower overall exit count. Local capital
per venture-backed startup is also considerably lower, though
we see a higher percentage of Illinois deals led by an outside
investor. Finally, the data suggests approximately one in three
founders attended an in-state four-year institution, indicating a
healthy pipeline of local talent, as well as a relatively high student
population per capita.
Last, we examine Florida as an example of an ecosystem in
early stages of development. Though startups in Florida have
consistently closed over 200 rounds in each year from 2015 to
2017, capital raised in these rounds mostly has been small values
across early-stage companies with a few exceptions, including
Florida's state unicorn, Magic Leap, a mixed reality company.
This observation is reflected across Florida's startup density per
capita, with a very low representation of late-stage companies.
Local capital is also low; however, costs of operation are closer
to the average than the previous two examples, an advantage to
Florida startups. Similar to California, Florida is home to a diverse
population, signaling promise for potential business creation and
diversity of thought among founders. Local talent retention is low
compared to other regions, however, which may be related to the
low density of startups in the region.
Discussion of data
These metrics focus exclusively on indicators of venture-backed
startup ecosystems, which are merely a subsector of many
regions' business markets. We recognize the selected variables
do not capture all factors of a regional venture ecosystem. Due
to data availability, we cannot include every important variable
in this model. Certain relevant metrics, such as cost of operations
in a region, are limited to minimal indicators such as housing
and wages. These metrics also do not consider historical metrics
that lead to development, including the existence of large,
longstanding businesses in the region, such as Google in California
or Microsoft in Seattle.
9
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
PitchBook's proprietary dataset is a comprehensive aggregation
of venture deals, but there is likely underreporting in certain
areas due to the general lack of transparency in private markets.
Data provided by government agencies is also noted to have
inherent reporting and sampling bias, though we regard this data
to be of sound and consistent quality. For the sake of uniformity
across datasets, we use state-level data. We also exclude the
presentation of states with less than 30 unique companies that
received venture funding in 2017.18
Additionally, we note that all interpretations are our own, and we
welcome opinions that illuminate perspectives not mentioned
here. We also welcome comments and feedback on relevant,
measurable indicators we missed for further development of these
variables and general framework.
18: This data can be made available upon request for PitchBook clients. The excluded states
include: AK, AR, HI, ID, LA, ME, MS, MT, ND, NE, NH, OK, RI, SD, VT, WV, WY
10
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Population
Angel/seed/acc/inc density
Early-stage density
Late-stage density
Alabama
4,874,747
5.54
0.62
0.41
Arizona
7,016,270
12.83
2.57
1.43
California
39,536,653
52.84
29.09
15.61
Colorado
5,607,154
42.27
11.41
8.92
Connecticut
3,588,184
15.05
9.75
4.18
Delaware
961,939
62.37
16.63
2.08
District of Columbia
693,972
77.81
25.94
10.09
Florida
20,984,400
9.67
2.62
1.24
Georgia
10,429,379
9.68
2.88
3.55
Illinois
12,802,023
13.12
5.78
4.14
Indiana
6,666,818
9.45
2.10
2.55
Iowa
3,145,711
8.90
2.86
0.95
Kansas
2,913,123
5.84
3.09
2.40
Kentucky
4,454,189
6.96
1.12
1.35
Maryland
6,052,177
16.85
5.95
3.14
Massachusetts
6,859,819
66.33
33.38
23.32
Michigan
9,962,311
8.43
2.21
1.81
Minnesota
5,576,606
9.68
4.66
2.33
Missouri
6,113,532
9.00
3.44
1.47
Nevada
2,998,039
9.34
2.33
1.67
New Jersey
9,005,644
11.22
3.89
1.44
New Mexico
2,088,070
11.97
1.44
3.35
New York
19,849,399
42.07
18.74
8.92
North Carolina
10,273,419
13.43
3.02
3.02
Ohio
11,658,609
10.29
2.74
2.74
Oregon
4,142,776
19.31
5.55
6.28
Pennsylvania
12,805,537
15.31
5.39
3.83
South Carolina
5,024,369
3.58
1.99
1.59
Tennessee
6,715,984
11.47
3.72
1.64
Texas
28,304,596
14.38
4.27
2.51
Utah
3,101,833
19.99
10.64
6.77
Virginia
8,470,020
12.99
4.01
3.90
Washington
7,405,743
33.08
13.37
6.89
Wisconsin
5,795,483
11.22
2.42
2.07
Appendix
Table 1: Density
Return to text
Note: All values for 2017.
We also exclude the presentation of states with less than 30 unique companies that received venture funding in 2017.
11
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Local capital per venture-
backed startup*
Outside VC
participation*
Cost of labor (z-score)*
Cost of housing
(z-score)**
Alabama
$6,814
13%
0.56
-1.04
Arizona
$246,181
23%
0.65
-0.10
California
$5,479,920
21%
1.34
1.95
Colorado
$380,909
23%
1.32
0.92
Connecticut
$3,643,979
22%
0.55
1.44
Delaware
$94,002
48%
-0.40
0.29
District of Columbia
$10,969,771
44%
1.83
2.13
Florida
$285,806
21%
-0.16
0.08
Georgia
$372,879
34%
0.35
-0.16
Illinois
$1,482,211
30%
0.71
0.23
Indiana
$19,131
24%
-0.56
-0.81
Iowa
$7,860
36%
-0.59
-0.80
Kansas
$107,855
32%
-0.67
-0.65
Kentucky
$34,848
24%
-1.08
-1.06
Maryland
$335,876
31%
0.68
1.74
Massachusetts
$6,780,108
26%
1.16
1.67
Michigan
$1,450,673
33%
-0.71
-0.59
Minnesota
$216,763
34%
0.72
0.12
Missouri
$1,600,111
30%
-0.14
-0.72
Nevada
$ 60,340
33%
-0.41
0.18
New Jersey
$1,079,216
26%
0.98
2.05
New Mexico
$20,738
25%
-0.15
-0.87
New York
$2,801,531
39%
0.70
1.02
North Carolina
$662,165
23%
0.79
-0.53
Ohio
$723,375
26%
-1.01
-0.67
Oregon
$60,823
22%
-0.06
0.40
Pennsylvania
$356,948
22%
-0.07
-0.27
South Carolina
$13,416
14%
-0.02
-0.69
Tennessee
$1,077,574
25%
-0.44
-0.76
Texas
$239,428
24%
0.42
-0.08
Utah
$1,494,278
32%
-0.06
0.33
Virginia
$934,985
31%
1.27
0.84
Washington
$1,356,422
28%
3.58
0.95
Wisconsin
$447,272
23%
-0.49
-0.38
Table 2: Resources
Return to text
*All values for 2017
** All values for 2016
Note: We also exclude the presentation of states with less than 30 unique companies that received venture funding in 2017.
12
PitchBook 2Q 2018 Analyst Note: VC Ecosystems
Entrepreneurial
experience*
Local
talent
retention*
Student
population**
Minority
business
ownership
(proxy)***
Gender
representation*
Immigrant
population**
Alabama
1%
22%
6%
13%
12%
3%
Arizona
3%
21%
8%
17%
14%
14%
California
5%
26%
4%
34%
12%
27%
Colorado
3%
17%
7%
11%
13%
10%
Connecticut
3%
13%
5%
12%
11%
14%
Delaware
3%
3%
8%
16%
9%
9%
District of Columbia
4%
11%
16%
33%
18%
13%
Florida
4%
16%
7%
25%
11%
21%
Georgia
3%
20%
5%
22%
10%
10%
Illinois
4%
30%
5%
17%
12%
14%
Indiana
3%
28%
6%
9%
9%
5%
Iowa
2%
26%
7%
5%
13%
5%
Kansas
4%
21%
6%
9%
11%
7%
Kentucky
3%
20%
5%
7%
12%
4%
Maryland
4%
19%
5%
25%
13%
15%
Massachusetts
5%
37%
7%
12%
12%
16%
Michigan
3%
34%
5%
9%
12%
7%
Minnesota
3%
25%
7%
6%
12%
8%
Missouri
4%
17%
6%
10%
14%
4%
Nevada
2%
5%
4%
21%
11%
20%
New Jersey
4%
11%
3%
25%
10%
22%
New Mexico
3%
14%
4%
26%
16%
10%
New York
4%
22%
6%
24%
14%
23%
North Carolina
3%
26%
4%
13%
13%
8%
Ohio
3%
30%
5%
9%
10%
4%
Oregon
3%
14%
5%
12%
14%
10%
Pennsylvania
2%
34%
5%
10%
12%
7%
South Carolina
2%
17%
4%
12%
13%
5%
Tennessee
4%
21%
4%
12%
12%
5%
Texas
3%
27%
4%
29%
10%
17%
Utah
3%
39%
11%
7%
8%
8%
Virginia
3%
19%
6%
23%
13%
12%
Washington
4%
19%
5%
17%
13%
14%
Wisconsin
2%
26%
5%
6%
14%
5%
Table 3: Talent
*All values for 2017
** All values for 2016
***All values for 2015
Note: We also exclude the presentation of states with less than 30 unique companies that received venture funding in 2017.
We note that information regarding the university or college attended by founder(s) is known for only 59.5% of the dataset. The unknown data
for the remaining portion of the sample may create bias in the local talent retention variable.
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