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The Global Startup Ecosystem Report 2020 (GSER 2020)

Methodology, Definitions & References

The Startup Genome quantitative data infrastructure includes data on over 1.27 million companies, 250+ 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
    • 2017-2019 Startup Ecosystem Survey with more than 10,000 participants per year
    • 2020 COVID-19 Founder Survey with more than 2000 participants
    • 2020 COVID-19 Knowledge Base with more 200 articles on policies actions taken all over the world
  • Crunchbase: global dataset on funding, exits, and locations of startups and investors
  • Orb Intelligence: global dataset on company information
  • PitchBook: private capital market data provider
  • Dealroom: global dataset on funding, exits, and locations of startups and investors
  • Local partners (accelerators, incubators, startup hubs, investors):
    • list of startups
    • list of local exits and funding events

Data Sources

Primary Data Sources

In alphabetical order

  • Crunchbase (2017-2020). Crunchbase.com Database
  • CB Insights (2019-2020). Cbinsights.com Database
  • Dealroom.co BV. (2017-2020). Dealroom.co Database
  • Orb Intelligence Inc. (2017-2020). orb-intelligence.com Database
  • PitchBook (2018-2020), a private capital market data provider Database
  • Startup Genome LLC (2017-2020). StartupGenome.com 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 2017, 2018 and first half of 2019.
  • 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 2019 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 2017, 2018, and first half of 2019, 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%
  • 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 the timeframe 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+)
    • 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 $100M+ 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 (less than 5 years of activity) (in Q1 2020) and 50% ratio 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 1B by Metro population (in M)
    • 20% Log of Ratio of Exits over 50M in 2017-1H2019 to Funding Series A on 2017-1H2019
  • 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 lying 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 and Development Hospitals
    • 10% Count of Research and Development Locations
    • 10% Log of Average amount of LS specific Grants
    • 10% Log of Count of LS specific research organizations

Experience & Talent
37.5% Talent

Assesses the talent early-stage startups have access to.

  • 90% Tech Talent
    • 90% Quality & Access
      • 70% Log of Count of Exits over 50M 2009-2018
      • 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 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
  • 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
Captures the degree of startup experience in an ecosystem

  • 80% Startup Experience in Ecosystem
    • Log of Count of Funding of Series A in 2009-2019 (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 exits of $1B+
    • 40% Log of number of exits of $50M


Knowledge

The Knowledge Success Factor assesses:

  • 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 LS patents in ecosystem
    • 30% LS three year moving average growth of Patents.
    • 10% LS 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)

Growth Index

  • 50% Exit Growth Index: Index of growth in tech startup exits in the ecosystem from 2015-2016 to 2017-2018. Measured on a scale of 1-10
  • 50% Funding Growth Index: Index of growth in early-stage funding (Seed and Series A) in tech startups in the ecosystem from 2014-2015 to 2016-2017. Measured on a scale of 1-10


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 Eco 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 timeframe without double-counting
    • 20% Exits
      • 80% Volume of Exits (80% log of number of exits of $50M+ and 20% log of number of exits of $1B+)
      • 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 2017-1H2019)
      • 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 1B by Metro population (in M)
      • 20% Log of Ratio of Exits over 50M in 2017-1H2019 to Funding Ser A on 2017-1H2019

    Talent

    50% Talent
    Assesses the talent early-stage startups have access to.
    • 80% Tech Talent
      • 50% Quality & Access
        • 70% Log of Count of Exits over 50M 2009-2018
        • 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 2009-2019 (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 exits of $1B+
      • 40% Log of number of exits of $50M

    Notes on reason for changes of Ecosystem Value 

    Our constant endeavour at Startup Genome is to improve our quality of research and data to help our members and our readers to gain the 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 the ecosystem value. The major factors that influenced the ecosystem value exchange 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 also included CB insights data as well as introduced in-depth checks conducted by our team to ensure the tech classification done is indeed true. This resulted in more companies being tagged as tech and hence more deals being added in 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 of receiving higher and late stage funding rounds. With that in mind, for exits over $100M we included companies with formation date till 1995. Similarly, for rounds later than Series B, companies with formation dates till 1995 were taken into our data set. This added about 8.5% to the ecosystem vale of the top 100 ecosystems
    3. Increasing Unicorns Data: We made enormous strides in expanding unicorns coverage in our data set. This included incorporating CB insights unicorns and billion dollar exits (after in-depth checks by our team). 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 big deals of each ecosystem to make sure that deal was valid, reflected the true value and belonged to that particular ecosystem.

    Ecosystem Deep Dive Metrics

    Ecosystem Value

    A measure of economic impact, calculated as the value of exits and startup valuations over 2017, 2018, and the first half of 2019


    Exit Growth Index

    Index of growth in tech startup exits in the ecosystem from 2016-2017 to 2018-2019. Measured on a scale of 1-10, where 10 is the highest tier of growth observed and 1 is the lowest


    Funding Growth Index

    Index of growth in early-stage funding (Seed and Series A) in tech startups in the ecosystem from 2016-2017 to 2018-2019. Measured on a scale of 1-10, where 10 is the highest tier of growth observed and 1 is the lowest


    Total Early Stage Funding

    Total Seed and Series A funding in tech startups in 2017, 2018, and first half of 2019


    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 (2017, 2018, and first half of 2019)


    Median Seed

    Median of Seed rounds in tech startups in the ecosystem for a 2.5 year time period (2017, 2018, and first half of 2019)


    Investor Activity Index

    Index of level of investor activity in the ecosystem based on various metrics measuring volume of investors, experience of investors, and new investors for the first quarter of 2020.


    Key Concepts and Definitions

    Ranking Score

    The ranking is primarily driven by one question: In which ecosystems does an early-stage startup have the best chance of building a global success?


    Startup

    A technology-enabled business that is less than 10 years old. Steve Blank defines a startup as a “temporary organization in search for a repeatable and scalable business model.” We use this definition to look across sectors and sub-sectors, including software, hardware, health, energy, and others, and we use these parameters for data collection purposes.


    Startup Ecosystem

    A shared pool of resources, generally located within a 60-mile (100-kilometer) radius around a center point in a given region, with a few exceptions based on local reality. Resources typically include policymakers, accelerators, incubators, coworking spaces, educational institutions, and funding groups.


    Ecosystem Lifecycle Factors

    Combined with some of measures from our Success Factor Model, Ecosystem Lifecycle Factors measure different dimensions of a startup ecosystem. These allows us to determine the phase of development in which the ecosystem is in -- Activation, Globalization, Attraction, or Integration.

    • Resource Attraction: captures the extent to which entrepreneurs move to an ecosystem to start a startup and how many startups relocate to an ecosystem. Increasing Resource Attraction at the national and global levels is an important determinant of an ecosystem’s growth rate.
    • Startup Leakage: measures the percentage of startups that, in our global survey, reported leaving a certain ecosystem. A low score on Startup Leakage indicates that few startups have left that ecosystem in favor of another one.
    • Triggers: Triggers are the externally impressive exits and high startup valuations that spark a sharp increase in Resource Attraction, driving the growth of an ecosystem and its evolution to the next phase of the Lifecycle.

    Ecosystem Success Factors Model

    Our principal analytical tool, this measures different dimensions of what supports the performance of local startups. We look at multiple factors for our rankings: one measuring actual performance, with other Success Factors associated with performance, each comprised of sub-factors and metrics. These factors are highlighted in our Ranking Methodology section, as well as in each rankings 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 connectednes 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: 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 definition 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.

    For more detail, including in our machine learning classification of sub-sectors, please see our Startup Classification sub-section in this Methodology. For more coverage on each sub-sector, please see their respective sections in the report.


    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)

    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, 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 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 Technology refers to technology that can improve the construction processes and methods including productivity gains, cost savings, improved safety, shorter lead times and maximised resources etc. Property tech 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.


    Education Tech (Edtech)

    Education Technology 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.


    Government Tech (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 maximises 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).


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