The Global Startup Ecosystem Report 2021

Methodology

The Startup Genome quantitative infrastructure includes data on over three million companies, nearly 300 ecosystems, and survey data from more than 10,000 startup executives across the globe—the Voice of Entrepreneurs.


Below is a description of the main data sets that make up this infrastructure:

  • Startup Genome proprietary data:
    • Interviews with more than 100 experts
    • 2017-2019 Startup Ecosystem Survey with more than 10,000 participants per year
  • Crunchbase: Global data set on funding, exits, and locations of startups and investors
  • Dealroom: Global data set on funding, exits, and locations of startups and investors
  • PitchBook: Private capital-market data provider
  • Local partners (accelerators, incubators, startup hubs, investors):
    • list of startups
    • list of local exits and funding events

Data Sources

Primary Data Sources

  • Startup Genome LLC (2017-2021). StartupGenome.com Database
  • Crunchbase (2017-2021). Crunchbase.com Database
  • Dealroom.co BV. (2017-2021). Dealroom.co Database
  • CB Insights (2019-2021). Cbinsights.com Database
  • Orb Intelligence Inc. (2017-2021). orb-intelligence.com Database
  • PitchBook (2018-2021), a private capital market data provider Database

Secondary Data Sources

  • Forbes 2000
  • Github API
  • International IP Index
  • Meetup.com
  • OECD, R&D Spending
  • Other sources from Life Sciences Rankings
  • Salaries data from Glassdoor, Salary.com, and PayScale
  • Shanghai Rankings
  • Techboard
  • Times Higher Education Rankings
  • Top 800 R&D Hospitals, Webometrics
  • USPTO
  • WIPO
  • World Bank, Ease of Doing Business

Selected Data Timeframes

  • Ecosystem Value, Exit Value, and Startup Valuation: sum of exits and funding rounds of 2018, 2019, and the first half of 2020.
  • Based on our previous analysis we assessed that it takes one year for half of the seed rounds to find their way into major data sources. Therefore, we use the first half of 2020 as the latest period for which earlier-stage metrics can be computed to create reliable benchmarks at the ecosystem level.
  • Early-Stage Funding: Sum of all Seed and Series A investments in 2018, 2019, and the first half of 2020, corrected for obviously missing rounds.

Ranking Methodology (For Top Ecosystems)

Overall Ranking

The overall global ecosystem ranking is a weighted average of the following factor scores:

  • Performance: 30%
  • Funding: 25%
  • Market Reach: 15%
  • Connectedness: 5%
  • Talent & Experience: 20%
  • Knowledge: 5%

We calculated an ecosystem index value for each factor, based on the sub-factor and metrics detailed below. The ecosystems scores were multiplied by the above weights to establish the overall rank of each ecosystem. The weights of the factors were determined from 2017- 2020 through correlation analyses and modeling work based on linear regression analyses, using factor indexes as independent variables with the performance index as dependent variable. Finally, adding the actual Performance Index to the ranking formula serves to include the influence of unobserved factors on the performance of an ecosystem.


Ranking Details

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.
  • 50% Ecosystem Value
    • Log of sum of all exits and estimated startup valuations during the time frame without double-counting
  • 37.5% Exits
    • 80% Volume of Exits (80% log of number of exits of more than $50 million and 20% log of number of exits of more than $1 billion)
    • 20% Exit Growth Index (scored from 1 to 10)
  • 12.5% Startup Success
    • 60% Growth-Stage Success (100% Ratio of Series C-to-A Startups)
    • 30% Speed to Exit (50% average company age at exit and 50% average company age at IPO)
    • 10% Early-Stage Success (Ratio of Series B-to-A Startups)

Funding

Quantifies funding metrics important to the success of early-stage startups.

  • 90% Access
    • 90% Early-Stage Funding Volume (80% log of count and 20% log of sum of total early-stage funding deals)
    • 10% Log of Early-Stage Funding Growth
  • 10% Quality and Activity
    • 70% Volume of Investors (50% log of total number of VCs and CVCs in Q1 2020; and 50% Log of total number of large—over $100 million in AUM—VCs and CVCs in Q1 2020)
    • 10% Experience of Investors (50% number of investors with above average exit rates in Q1 2020; and 50% average years of experience of investors in Q1 2020)
    • 20% New Investor (50% log of total number of new investors with less than five years of activity in Q1 2020; and 50% ratio of active investors in Q1 2020)

Market Reach

Measures early-stage startup access to customers allowing them to scale and “Go-Global”.

  • 60% Globally Leading Companies
    • 50% Ratio of billion-dollar club to GDP (B)
    • 30% Ratio of exits over 50M to GDP (B)
    • 20% Log of ratio of exits over $50 million in 2018, 2019, and the first half of 2020 to funding Series A in 2018, 2019, and the first half of 2020
  • 30% Local Market Reach
    • Log of GDP of country
  • 10% Quality
    • Log of Commercialization of Tangible IP Assets (tiers from 1 to 10, score based on the International IP Index, measured at the country level)

Connectedness

Measures how connected the ecosystem is to the global fabric of knowledge within the ecosystem (Local Connectedness and Innovation Infrastructure).

  • 90% Local Connectedness
    • 60% Log of count of Meetup Groups on meetup.com
    • 40% Log of ratio of Number of Meetup Groups from meetup.com by population (in M)
  • 10% Infrastructure
    • 60% Log of Life Sciences-focused measure of accelerators and incubators
    • 10% Log of count of Research & Development Hospitals
    • 10% Count of Research & Development Locations
    • 10% Log of average amount of Life Sciences-specific grants
    • 10% Log of count of Life Sciences-specific research organizations

Experience & Talent

37.5% Talent

Assesses the talent to which early-stage startups have access

  • 90% Tech Talent
    • 90% Quality & Access
      • 70% Log of count of exits over $50 million from 2010 to 2019
      • 10% Share of top Github coders to total Github coders
      • 10% Log of Count of Github coders on github.com with more than 10 followers
      • 10% English proficiency score
    • 10% Cost
      • 50% Log of software-engineer salaries—lower is better—from Glassdoor, Salary.com, and PayScale
      • 50% Log of funding runway: ratio of median Series A funding rounds by software-engineer salary
  • 10% Life Sciences
    • 50% STEM students: Log of number of STEM students
    • 40% Life Sciences Access
      • 70% Log of number of Life Sciences disciplines
      • 30% Log of number of institutes that have Life Sciences-related disciplines
    • 10% Life Sciences Quality
      • 25% Average of CNCI score from Shanghai Rankings
      • 25% Average of TOP score from Shanghai Rankings
      • 25% Average IC score from Shanghai Rankings
      • 25% Average PUB Score from Shanghai Rankings

62.5% Experience

Captures the degree of startup experience in an ecosystem

  • 80% Startup experience in ecosystem
    • Log of count of Funding of Series A from 2010 to 2020
  • 20% Scaling experience in ecosystem (the cumulative number of significant exits, over $50 million and $1 billion, over 10 years for startups founded in the ecosystem)
    • 60% Log of number of exits of more than $1 billion
    • 40% Log of number of exits of $50 million

Knowledge

Measures innovation through research and patent activity

  • 80% Patents (the volume, complexity, and potential of patents in Life Sciences created in the ecosystem, further described in the Life Sciences section of the Methodology)
    • 50% Log of tier of number of Life Sciences patents in ecosystem
    • 30% Life Sciences three-year moving average growth of patents
    • 10% Life Sciences technology potential: a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces
      • 20% Complexity of technology class, based on a PageRank algorithm
      • 30% Global growth of technology class
      • 50% Size of technology class (log of number of global patents in class)
    • 10% Complexity score of patents: a measure of the capacity of the ecosystem to produce patents in complex technology classes, based on a PageRank algorithm
  • 20% Research (H-index, a measure of publication impact, this metric looks at the production of Life Sciences research at the country level)


Emerging Ecosystems Rankings

Emerging ecosystems are those ecosystems following the Top 40 Global Ecosystems in performance. The factor weights used to rank these ecosystems are slightly different from those used with top ecosystems (detailed in our Methodology section) to reflect their emerging status and emphasize factors that are more influential in ecosystems just beginning to grow.


The emerging ecosystem ranking is a weighted average of the following factor scores:

Performance: 45%
Funding: 30%
Market Reach: 15%
Talent & Experience: 10%

Emerging Ecosystem Ranking Details:

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.
  • 70% Ecosystem Value
    • Log of sum of all exits and estimated startups valuations during the time frame without double-counting
  • 20% Exits
    • 80% Volume of Exits (80% log of number of exits of more than $50 million and 20% log of number of exits of more than $1 billion)
    • 20% Exit Growth Index (scored from 1 to 10)
  • 10% Startup Success
    • 80% Growth-Stage Success (50% Ratio of Series C-to-A Startups and 50% log of unicorns from 2018-1H2020)
    • 10% Speed to Exit (50% average company age at exit and 50% average company age at IPO)
    • 10% Early-Stage Success (Ratio of Series B-to-A Startups)

Funding

Quantifies funding metrics important to the success of early-stage startups.

  • 100% Access
    • 90% Early-stage funding volume (80% log of count and 20% log of sum of total early-stage funding deals)
    • 10% Log of early-stage funding growth

Market Reach

Measures early-stage startup access to customers allowing them to scale and “Go-Global.”

  • 100% Globally Leading Companies
    • 50% Ratio of billion-dollar club to GDP (B)
    • 30% Ratio of exits over 50M to GDP (B)
    • 20% Log of ratio of exits over $50 million in 2018-1H2020 to Funding Series A on 2018-1H2020

Talent

50% Talent
  • 80% Tech Talent
    • 50% Quality & Access
      • 70% Log of count of exits over $50 million from 2009 to 2018
      • 10% Share of top Github coders to total Github coders
      • 20% Log of count of coders on Github.com with more than 10 followers
    • 50% Cost
      • 50% Log of software engineer salaries—lower is better—from Glassdoor, Salary.com, and PayScale
      • 50% Log of funding runway: ratio of Median Series A funding rounds by software-engineer salary
  • 20% Life Sciences
    • 100% STEM students: log of number of STEM students

50% Experience

Captures the degree of startup experience in an ecosystem

  • 80% Startup experience in ecosystem
    • Log of count of funding of Series A from 2010 to 2020
  • 20% Scaling experience in ecosystem (the cumulative number of significant exits, over $50 million and $1 billion, over 10 years for startups founded in the ecosystem)
    • 60% Log of number of exits of more than $1 billion
    • 40% Log of number of exits of $50 million

Notes On Reason For Changes Of Ecosystem Value

Our constant endeavour at Startup Genome is to improve the quality of research and data to help our members and our readers gain the best ‘on the ground’ knowledge about the world of startup ecosystems. With that aim in mind, we have significantly improved our data set in terms of both exhaustiveness and quality. As we improved the data, one key outcome was an increase in the Ecosystem Value. The major factors that influenced those changes are:


  1. Technology Startup Classification: We have made significant improvements in our classification of technology companies by adding more comprehensive classification criteria and tags from multiple sources. We have added CB insights data and introduced in-depth checks conducted by our team to ensure the tech classification is accurate. This resulted in more companies being tagged as tech and hence more deals added to our data set. This contributed approximately 8% to the Ecosystem Value increase.
  2. Increasing the Age Criteria: We concluded that older startups are more likely to receive higher and late-stage funding rounds. With that in mind, for exits over $100 million we included companies with formation dates that go back to 1995. Similarly, for rounds later than Series B, companies with formation dates back to 1995 are now included in our data set. This added about 8.5% to the Ecosystem Value of the top 100 ecosystems
  3. Increasing Unicorn Data: We made enormous strides in expanding unicorn coverage in our data set. This includes incorporating CB insights unicorns and billion-dollar exits (after in-depth checks by our team). That contributed about a 36% increase in Ecosystem Value to the top ecosystems.
  4. Fine Combing through Big Deals: As a final check, we scrupulously worked on the larger deals of each ecosystem to make sure that deal was valid, reflected the true value, and belonged to that particular ecosystem.

Ecosystem Page Metrics

Ecosystem Value

A measure of economic impact, calculated as the value of exits and startup valuations over 2018, 2019, and the first half of 2020


Total Early-Stage Funding

Total Seed and Series A funding in tech startups in 2018, 2019, and the first half of 2020


Software Engineer Salary

Average software engineer salary (lower is better): from Glassdoor, Salary.com, and PayScale; as well as local sources when applicable


Median Series A

Median of Series A rounds in tech startups in the ecosystem for a two-and-a-half-year time period (2018, 2019, and the first half of 2020)


Median Seed

Median of Seed rounds in tech startups in the ecosystem for a two-and-a-half-year time period (2018, 2019, and the first half of 2020)



Key Concepts And Definitions

Ranking The ranking compares ecosystems based on where early-stage startups will most likely build globally successful companies.

Startup Steve Blank defines a startup as a “temporary organization designed to search for a repeatable and scalable business model.” We use this definition to look at new businesses in Sectors and Sub-Sectors that include Deep Tech, Life Sciences, and Cleantech.

Ecosystem A cluster of startups and related entities that draw from a shared pool of resources and generally reside within a 60-mile (100-kilometer) radius of a central point in a particular region. The goal of the ecosystem is to launch and grow companies.

Ecosystem Success Factor Model Our principal analytical tool measures the dimensions that contribute to startup performance. We look at multiple Factors for our rankings: one measuring actual performance, with other Success Factors associated with performance, each composed of sub-Factors and metrics. These Factors are highlighted in our Ranking Methodology section.

  • Performance: A combination of leading, lagging, and current indicators that capture economic outcomes in a startup ecosystem
  • Funding: The level and growth of early-stage funding, looking at both access and quality
  • Market Reach: Early-stage startup access to customers allowing them to scale and “Go-Global”
  • Connectedness: Connectedness within the ecosystem and the supporting infrastructure  
  • Resource Attraction: The gravitational pull of an ecosystem drawing in entrepreneurs and startups from elsewhere
  • Startup Experience: The depth and diversity of the pool of prior startup experience in an ecosystem
  • Talent: The accessibility, quality, and cost of software engineering expertise
  • Founder: Success factors related to the startup founder, under his or her control, or internal to the startup as opposed to external (a function of the ecosystem)
    • Founder DNA: The background, experience, ambition, and motivation of local founders
    • Founder Go-Global Strategy: Whether a startup is going global from the outset or first targets its local market and whether its customer acquisition team is located, targeted, and skilled to succeed
    • Founder with High Ambition: Founders who expressed all of the following attributes: a total addressable market of $30 billion USD or more; development of a globally new or globally leading or niche product; a mission to change the world, get rich or create a great product
    • Founders with Experience in Sub-Sector: Founders who considered their graduate or postgraduate degrees to be directly relevant to their startups
  • Local Connectedness: A multi-variable assessment of the local community, including sense of community, relationships, and collisions between founders, investors, and experts
    • Sense of Community Index: A Sub-Factor of Local Connectedness capturing the degree to which founders informally receive help from investors, experts, and fellow founders
    • Number of Relationships Between Founders: Number of quality relationships between local founders, meaning they know each other and can call upon one other for help “this week”
    • Collision Index: A Sub-Factor of Local Connectedness capturing the number of tech events on Meetup.com and the density of tech events per startup in the ecosystem

Sector And Sub-Sector Definitions

Below are our definitions for each Startup Sub-Sector analyzed here. Note that Sub-Sectors are not mutually exclusive nor comprehensive. Some startups are in Sub-Sectors we did not consider.

In addition, at least based on patents, the data shows a clear tech convergence. Technology like AI software are increasingly inter-related, and we would expect a similar convergence overtime for Startup Sub-Sectors.


Advertising Tech (Adtech)

Advertising Tech includes different types of analytics and digital tools used in advertising and marketing. Extensive and complex systems are used to direct, convey, or monitor advertising to target audiences of any size and scale.


Advanced Manufacturing & Robotics

Advanced Manufacturing involves smart technology to improve traditional manufacturing of products and/or processes. Robotics is the science and technology of robots, their design, manufacture, and application.


Agriculture Tech (Agtech) & New Food

Agriculture Tech captures the use of technology in agriculture, horticulture, and aquaculture with the aim of improving yield, efficiency, and profitability through information monitoring and analysis of weather, pests, and soil and air temperature.


Artificial Intelligence (AI), Big Data & Analytics

AI, Big Data & Analytics refers to an area of technology devoted to extracting meaning from large sets of raw data, often including simulations of intelligent behavior in computers..


Blockchain

Blockchain is a decentralized data storage method secured by cryptography. Cryptocurrencies are one of many innovations utilizing the blockchain. Companies building their product/architecture on top of this decentralized and encrypted technology are defined as blockchain companies.


Cleantech

Cleantech consists of sustainable solutions in the fields of Energy, Water, Transportation, Agriculture, and Manufacturing that include advanced materials, smart grids, water treatment, efficient energy storage, and distributed energy systems.


Construction And Property Tech

Construction Tech can improve construction companies’ processes and methods, offering productivity gains, cost savings, better safety, shorter lead times, and maximised resources. Property tech helps organizations and individuals research, buy, sell, rent, lease, and manage real estate. Applications include searching for property, listing available properties, setting up viewing dates, and finalizing lease agreements and deals.


Consumer Electronics Or Home Electronics (includes Wearables, Smart Devices)

Consumer Electronics or Home Electronics comprises electronic or digital equipment intended for everyday use, including smart devices used for entertainment, communications, and home-office activities, as well as other wearables.


Cybersecurity

Cybersecurity is the body of technologies, processes, and practices designed to protect networks, computers, programs, and data from attack, damage, or unauthorized access.


Education Tech (Edtech)

Education Tech is devoted to the development and application of tools (including software, hardware, and processes) intended to redesign traditional products and services in education.


Fintech

Fintech aims to improve existing processes, products, and services in the Financial Services industry (including insurance) via software and modern technology.


Gaming

Gaming involves the development, marketing, and monetization of video games, gambling machines, and associated services.


Government Tech (Govtech)

Govtech is the technology infrastructure used by governments and government institutions to improve public service to citizens. It increases transparency and maximizes public welfare and involvement.


Life Sciences

Life Sciences is the sector concerned with diagnosing, treating, and managing diseases and conditions. It includes startups in Biotech, Pharma, and Medtech (also referred to as medical devices).