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Global Fintech Ecosystem Report 2020

Methodology & References

The Startup Genome quantitative data infrastructure includes data on over 1.5 million companies, 270+ ecosystems, and survey data from more than 15,000 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 with 100+ experts
    • 2017-2020 Startup Ecosystem Survey with more than 10,000 participants per year
    • 2020 COVID-19 Founder Survey with more than 2,000 participants
    • 2020 COVID-19 Knowledge Base with more 200 articles on policy 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
  • CB Insights: global dataset on unicorns


Subsector Definition

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

List of Verticals falling under Fintech:

  • Insurance Tech
  • Risk Management
  • Trading, Portfolios
  • Personal Finance
  • Banking, Accounting
  • Digital Currency
  • Cryptocurrency
  • Data-driven finance
  • Phone banking
  • Robo-advisory
  • Regtech


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

  • Exit and Funding: count of exits, early-stage funding and Series B rounds of 2009 to 2020.
  • Based on our previous analysis we assessed that it takes one year for half of the seed rounds in a given period 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.


Fintech Sub-sectors Ranking Methodology (For Top Ecosystems)

Overall Ranking

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

  • Performance: 30%
  • Funding: 20%
  • Talent and Experience: 20%
  • Focus: 20%
  • Legacy: 10%

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 2009-2020 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.

  • 30% - Number of exits in the time period of 2015 - 2020
  • 20% - Number of exits over $50M in the time period of 2015 - 2020
  • 40% - Startup output comprising of the number of startups in the period of 2009-2019
  • 10% - Unicorns comprising of all companies with over $1B value in time period 2015-2020

  • Funding

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

  • 90% Early Stage Funding (ESF) in the time period of 2015-2020
  • 10% Series B Funding in the time period of 2015-2020

  • Talent and Experience
    50% Talent
    Captures the number and quality of Fintech-related university programmes.
    • 36% - Average quality score of Shanghai university rankings (Fintech related)
    • 6% - Average of CNCI score from Shanghai Rankings (Fintech related)
    • 18% - Average of TOP score from Shanghai Rankings (Fintech related)
    • 20%- Number of universities (Fintech related)
    • 20% - Number of degree programs (Fintech related)

    50% Experience
    Captures the degree of startup experience in an ecosystem
    • 60% - Exits over $50M in the period of 2009-2019
    • 40% - Series A funding rounds in the period of 2009-2019

    Focus
    The focus of an ecosystem in the Fintech sub-sectors
    • 100% - Startup Output share of Fintech

    Legacy
    Legacy is represented by the presence of sub-sectors related to large corporations in the ecosystem.
    • 50% - Sum of market value for all companies in the ecosystem
    • 50% - Sum of employees for all companies in the ecosystem

    Understanding the Model: Why You Should Care About Measuring Your Startup Ecosystem?

    The underlying drive for the Global Startup Ecosystem Report is to answer three key questions founders, investors, and policymakers all over the globe have been asking:

    • Where are the top-performing ecosystems in the world? Where do early-stage startups have the best shot at building global success?
    • Why are some places on the rise while others are falling behind?
    • How can ecosystems increase their chances of winning in the global startup revolution?

    Since starting out as a research project with Steve Blank, the father of the Lean Startup movement, and Prof. Chuck Eesley at Stanford University in 2011, we have been on a mission to codify and understand what factors make startup ecosystems successful so more places have a chance of creating and capturing their fair share of the value created by the global startup revolution.

    We have made tremendous progress since then and continue to evolve and improve. What you are looking at is by far the deepest, most comprehensive startup ecosystem research ever done — by Startup Genome, the Global Entrepreneurship Network, and in our field in general.

    Among other things, this year’s research has:

    • Nearly doubled the number of ecosystems studied since our last published rankings — assessing over 270 ecosystems across over 100 countries to rank the top 40 globally and produce a ranking of the top 100 emerging ecosystems. By expanding our list of ecosystems covered, we made inroads in increasing our coverage of Africa and South America as well.
    • Incorporated several real-time data sources into our broader model, from Google Trends to track public discourse on startups, to GitHub to identify active programmers, to Meetup data for measuring local tech events.
    • Conducted in-depth research on how the COVID-19 crisis is impacting startups and startup ecosystems. Through our COVID-19 impact survey, the first ever on the topic, we were able to draw numerous insights that provided the first learnings about the crisis and its impact on startups and startup ecosystems.
    • Collected information on public policy actions taken by governments to support startup ecosystems during the COVID-19 crisis. Our audit of public policy responses taken by the governments created a knowledge base with 200+ COVID-19-related policy measures.

    Bringing all this together, Startup Genome’s broader Ecosystem Assessment Framework now includes more than 100 metrics, which better capture the ecosystem factors that drive startup performance.

    For the 2020 rankings specifically, we measured a total of seven success factors, listed below (see the Methodology section for more details).

    • Performance
    • Funding
    • Talent & Experience
    • Focus
    • Legacy
    The three main questions we start this section with unfold into many strategic questions. Startup Genome’s ecosystem assessment framework can help answer them. For example: 


    For founders and startup executives:

    • Where should I create my tech startup to maximize my chances of success?
    • Where should I open a second office?
    • Where can I get the most bang for my buck in terms of cost?


    For investors:

    • Where do startups have the best odds of raising additional funding?
    • Which high-performing ecosystems have a gap in experienced local investors that I might be able to benefit from?

    For policymakers:

    • How should I measure the progress of our startup ecosystem?
    • What are the biggest gaps in our startup economy that I want to focus on first?


    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 of 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 realities. Resources typically include policymakers, accelerators, incubators, coworking spaces, educational institutions and funding groups.


    Ecosystem Success Factors Model

    Our principal analytical tool, this measures different dimensions that support 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 comprising 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.
    • 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.


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