Methodology

The Startup Genome quantitative data 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 datasets that make up this data science infrastructure:

• Startup Genome proprietary data:
  • Interview of 100+ Experts
  • 2017-2021 Startup Ecosystem Survey with more than 10,000 participants per year
    
• Dealroom: global dataset on funding, exits, and locations of startups and investors
• Crunchbase: global dataset 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 (2017-2022). StartupGenome.com Database
• Dealroom.co BV. (2017-2022). Dealroom.co Database
• Crunchbase (2017-2022). Crunchbase.com Database
• CB Insights (2019-2022). Cbinsights.com Database
• Orb Intelligence Inc. (2017-2021). orb-intelligence.com Database
• PitchBook (2018-2022), 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 are from H2 2019–2021.
• Based on our previous analysis, we know that it takes around one year for 50% of seed rounds to appear in the major data sources. As such, we use H2 2020 as the most recent period for seed rounds and earlier-stage metrics that are computed to create reliable benchmarks at the ecosystem level.
  
• For early-stage funding, we took the count of all seed and Series A investments in H2 2018–2020 for seed rounds and H2 2019–2021 for series A rounds. It takes four to eight weeks for the majority of Series A rounds to appear in our sources.
  
  

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%
• Experience & Talent: 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 startups valuations during H2 2019–2021 without double-counting
    
• 37.5% Exits
  • 80% Volume of Exits (80% log of number of exits of $50M+ and 20% log of number of exits of $1B+) from H2 2019–2021.
  • 20% Exit Growth Index (scored from 1 to 10) from 2018–2019 vs. 2020–2021
    
• 12.5% Startup Success
  • 60% Growth-Stage Success (100% Ratio of Series C-to-A Startups) from H2 2019–2021.
  • 30% Speed to Exit (50% average company age at exit and 50% average company age at IPO) from H2 2019–2021.
  • 10% Early-Stage Success (Ratio of Series B-to-A Startups) from H2 2019–2021.

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). Time range for Seed rounds is Jul 01, 2018 to Dec 31, 2020 and for Series A time range is Jul 01, 2019 to Dec 31, 2021  
  • 10% Log of Early-Stage Funding Growth from 2018–2019 vs. 2020–2021
    
• 10% Quality and Activity
  • 70% Volume of Investors (50% log of total number of VCs and CVCs [in Q1 2022]; and 50% log of total number of large $100 million+ AUM VCs and CVCs [in Q1 2022])
  • 10% Experience of Investors (50% number of investors with above average exit rates [in Q1 2022] and 50% average years of experience of investors [in Q1 2022])
    
  • 20% New Investor (50% log of total number of new investors [in Q1 2022, with less than five years of activity] and 50% ratio of active investors [in Q1 2022])
    

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) in H2 2019–2021.
  • 30% Ratio of exits over $50M by Metro population (in M) in H2 2019–2021.
  • 20% Log of Ratio of Exits over $50 million in H2 2019–2021 to Funding Series A in H2 2019–2021
    
• 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) for 2022.
    

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 millions)
    
• 10% Infrastructure
  • 90% Log of Life Sciences-focused measure of accelerators and incubators
  • 10% Log of Count of Research and Development Hospitals
    

Experience & Talent

37.5% Talent

• 90% Tech Talent
  • 90% Quality & Access
    • 70% Log of Count of Exits over $50 million in 2012–2021
    • 10% Share of top github coders to total github coders (based on the data available in January 2022)
    • 10% Log of Count of Github coders on github.com with more than 10 followers (based on the data available in January 2022)
    • 10% English Proficiency Score for 2022
      
  • 10% Cost
    • 50% Log of software engineer salary — lower is better — from Glassdoor, Salary.com, and PayScale for 2022
    • 50% Log of Funding Runway: Ratio of Median Series A funding rounds for H2 2019–2021 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 which 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

• 80% Startup Experience in Ecosystem
  • Log of Count of Funding of Series A in 2012–2021 (10 years)
    
• 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 $1 billion+exits in 2012–2021 (10 years)
  • 40% Log of number of $50 million+ exits in 2012–2021 (10 years)
    

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). The time frame for this metrics is:
  • 50% Log of Tier of number of Life Sciences patents in ecosystem 2011–2020 (10 years)
  • 30% Life Sciences three-year moving average growth of Patents 2013–2015 vs. 2016–2018.
  • 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 for producing patent 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) for 2022.



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 the factors that influence more in ecosystems that are just beginning to grow.

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

• Performance: 45%
• Funding: 30%
• Market Reach: 15%
• Experience & Talent: 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 H2 2019–2021 without double-counting
• 20% Exits
• 80% Volume of Exits (80% log of number of $50 million+ exits and 20% log of number of $1 billion+ exits) in H2 2019–2021.
• 20% Exit Growth Index (scored from 1 to 10) for 2018–2019 vs. 2020–2021
  
• 10% Startup Success
• 80% Growth-Stage Success (50% Ratio of Series C-to-A Startups and 50% log of unicorns from H2 2019–2021)
  
• 10% Speed to Exit (50% average company age at exit and 50% average company age at IPO) from H2 2019–2021.
• 10% Early-Stage Success (ratio of Series B to Series A startups) from H2 2019–2021.



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 from H2 2019–2021.
• 10% Log of Early-Stage Funding Growth from 2018–2019 vs. 2020–2021
  



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 (in billions) from H2 2019–2021
• 30% Ratio of $1 billion+ exits by Metro population (in millions) from H2 2019–2021
• 20% Log of Ratio of $50 million+ exits in H2 2019–2021 to Series A funding in H2 2019–2021
  



Talent

50% Talent

Assesses the talent early-stage startups have access to.

• 80% Tech Talent
  • 50% Quality & Access
    • 70% Log of Count of $50 million+ exits from 2012–2021
    • 10% Share of top github coders to total github coders
    • 20% Log of Count of Github coders on github.com with more than 10 followers
  • 50% Cost
    • 50% Log of software engineer salary — 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 in 2012-2021 (10 years)
  
• 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 $1 billion+ exits in 2012–2021 (10 years)
• 40% Log of number of $50 million+ exits in 2012–2021 (10 years)
  



Notes on Changes in Ecosystem Value

Our constant endeavor at Startup Genome is to improve our quality of research and data to help our members and our readers gain absolute, on the ground knowledge into the world of startup ecosystems. With that aim in mind, we have significantly improved our data set — both in terms of exhaustiveness and quality. As we improved the data, one of the key outcomes was an increase in Ecosystem Value. The major factors that influenced this 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 to ensure the tech classification is accurate. This resulted in more companies being tagged as tech and hence more deals added to our dataset. This contributed approximately 8% to Ecosystem Value.
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 until 1995 are now included in our dataset. This added about 8.5% to the Ecosystem Value of the top 100 ecosystems
3. Increasing Unicorns Data: We have made enormous strides in expanding unicorns coverage in our dataset. This includes incorporating CB Insights unicorns and $1 billion+ exits (after in-depth checks). This contributed to about 36% in the increase in Ecosystem Value of 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.
5. From this year onwards, we are including exits larger than $500 million that took place after 2018. These large exits stay in their ecosystem, mostly in the form of dry powder for investors to expand their portfolios, an important effect to take into account.
6. In previous years, we have only considered the ecosystem that a startup is founded in. From this year, we have also added the value of the top five startups and/or unicorns to the ecosystem where the startup is headquartered. The intention is to attribute both where a startup is born and where it creates attraction.



Ecosystem Page Metrics

Ecosystem Value

A measure of economic impact, calculated as the value of exits and startup valuations over the first half of 2019, 2020, 2021.


Total Early-Stage Funding

Total Seed and Series A funding in tech startups in H1 2019–2021.


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 2.5 year time period (H1 2019–2021).


Median Seed

Median of Seed rounds in tech startups in the ecosystem for a 2.5 year time period (H1 2019–2021).




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 Software, Hardware, Health, and Energy.


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: Measures early-stage startup access to customers allowing them to scale and “Go-Global.”
• Connectedness: Measures how connectedness within the ecosystem and the supporting infrastructure  
• Resource Attraction: The gravitational pull of an ecosystem in drawing in entrepreneurs and startups from elsewhere.
• Startup Experience: The depth and diversity of the pool of prior startup experience in an ecosystem.
• Talent: Measures 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: Measures 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: Total Addressable Market of $30 billion USD or more; developing a globally-new, or one of the globally-leading or niche products; and the 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 degree to be directly relevant to their startup.
  
• 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: The number of quality relationships between local founders, where they know each other and can call upon the 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 from 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 captures different types of analytics and digital tools used in the context of 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

Agtech 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. New Food includes technologies that can be leveraged to create efficiency and sustainability in designing, producing, choosing, delivering, and consuming food. This is informed by Forward Fooding's definition of Food Tech.



Artificial Intelligence, Big Data & Analytics

AI, Big Data & Analytics refers to an area of technology devoted to extracting meaning from large sets of raw data, e.g. 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 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 (Proptech)

Construction Technology refers to technology that can improve the construction processes and methods including productivity gains, cost savings, improved safety, shorter lead times and maximized resources etc. Proptech refers to the technology that helps organizations and individuals research, buy, sell, rent, lease and manage real estate. Methods include searching for property, listing available properties, setting up viewing dates and finalizing the lease agreements and deals.

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

Consumer Electronics or Home Electronics are 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.

Edtech

Edtech refers to an area of technology 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 and gambling machines, as well as associated services.

Govtech

Govtech is the infrastructure of technology that the governments and government institutions use to provide specific services to its citizens with the aim of improving public service. This technology enables the government to effectively operate in a way that increases transparency and maximizes public welfare and involvement.

Life Sciences

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