Methodology

Our quantitative data infrastructure is the world’s most comprehensive and quality controlled.
We study 3.5 million companies across 290+ startup ecosystems, combine data from the three leading venture funding databases and then remove duplicates and clean with an AI engine, machine learning techniques, and a manual review. Additionally, we work with 50+ countries to power and update the data found in our reports and policy consultancy work.

Key Definitions

Sub-Sector Definitions

Ecosystem Page Metrics

Ecosystem Value: A measure of economic impact, calculated as the value of exits and startup valuations from H2 2020–2022. Ecosystem Value growth (CAGR) is calculated based on companies founded in the ecosystem in H2 2018–H2 2020 vs. H2 2020–H2 2022.

Total Early-Stage Funding: The total seed and Series A funding in tech startups in H2 2020–2022.

Total VC Funding: The total VC funding (seed, Series A, Series B+) in tech startups in H2 2018–2022.

Median Series A: The median of Series A rounds in tech startups in the ecosystem in H2 2020–2022.

Median Seed: The median of seed rounds in tech startups in the ecosystem in H2 2020–2022.

Software Engineer Salary: Average software engineer salary informed by data from Glassdoor, Salary.com, and PayScale, as well as local sources when available.

Time to Exit: The average age at the time of exit in the ecosystem in 2018–2022.

For additional definitions, please see the Glossary on our website.

Primary Data Sources

• Startup Genome proprietary data:
• Interviews of 100+ experts
• 2017–2022 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
• Startup Genome LLC (2017-2023). StartupGenome.com database
  
• Dealroom.co BV. (2017-2023). Dealroom.co database
  
• Crunchbase (2017-2023). Crunchbase.com database
  
• CB Insights (2019-2023). Cbinsights.com database
  
• Orb Intelligence Inc. (2017-2023). orb-intelligence.com database
  
• PitchBook (2018-2023), 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
• USPTO
• WIPO
• World Bank
  

Selected Data Timeframes

• Ecosystem Value: Sum of exits and funding rounds in H2 2020–2022.
• Based on long-term research and 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 take the count of all seed and Series A investments in H2 2019–2021 for seed rounds and H2 2020–2022 for Series A rounds. It takes four to eight weeks for the majority of Series A rounds to appear in our sources.
  

Ranking Methodology

Global Startup Ecosystem Ranking 2023 (Top 30 + Runners-Up)

This ranking identifies the top 40 ecosystems. These ecosystems are more mature than other ecosystems globally, featuring more exits over $50 million and more funding activities.

This 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 calculate an ecosystem index value for each factor, based on the sub-factor and metrics detailed below. The ecosystems scores are 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 indices as independent variables with the performance index as a 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 2020–2022 without double-counting.
    
• 37.5% Exits
• 80% volume of exits (80% log of number of $50 million+ exits and 20% log of number of $1 billion+ exits) from H2 2020–2022.
• 20% exit growth index (scored from 1 to 10) from 2019–2020 vs. 2021–2022.
• 12.5% Startup Success
• 60% growth-stage success (100% ratio of Series C-to-A Startups) from H2 2020–2022.
• 30% speed to exit (50% average company age at exit and 50% average company age at IPO) from H2 2020–2022.
• 10% early-stage success (ratio of Series B-to-A startups) from H2 2020–2022.
  

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). The time range for seed rounds is July 1, 2019 to December 31, 2021 and for Series A rounds is July 1, 2020 to Dec 31, 2022.
• 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 2022 and 50% log of total number of investors with $100 million+ assets under management in Q1 2022).
• 10% experience of investors (50% number of investors with above average exit rates and 50% average years of experience of investors.
• 20% new investors (50% log of total number of new investors, with less than five years of activity) and 50% ratio of active investors.
  

Market Reach

Measures early-stage startup access to customers, allowing them to scale and potentially “go-global.”

• 60% Globally Leading Companies
• 50% ratio of startups with $1 billion+ valuations to GDP in H2 2020–2022.
• 30% ratio of $50 million+ exits by Metro population (M) in H2 2020–2022.
• 10% log of ratio of exits over $50 million in the second half of 2020, 2021, and 2022 to Series A funding in the first half of 2020, 2021, and 2022.
• 10% ratio of tech startups (formed after 2012) with international secondary offices.
• 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).

• 60% Local Connectedness
  • 55% log of count of Meetup groups on meetup.com.
  • 15% log of ratio of number of Meetup groups from meetup.com by population (M).
  • 30% log of the number of accelerators and incubators.
    
• 40% Global Connectedness
  
• 90% log of tech companies with secondary offices in the ecosystem.
• 10% log of international investors.
  

Talent & Experience

Assesses the talent early-stage startups have access to and the degree of startup experience in an ecosystem.

• 37.5% Talent
  
• 80% Tech Talent
  
• 90% Quality & Access
  
• 70% log of count of $50 million+ exits in 2013–2022
• 10% share of top Github coders to total Github coders (based on the data available in January 2023).
• 10% log of count of Github coders on github.com with more than 10 followers (based on the data available in January 2023).
• 10% English Proficiency Score for 2022.
• 10% Cost
  
• 50% log of software engineer salary — lower is considered better — from Glassdoor, Salary.com, and PayScale for 2022.
• 50% log of funding runway: ratio of median Series A funding rounds for H2 2020–2022 by software engineer salary.
  
• 20% 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 with Life Sciences-related disciplines.
• 10% 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 the ecosystem.
  
• Log of count of funding of Series A in 2013–2022.
  
• 20% scaling experience in the 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.
• 40% log of number of $50 million+exits.
  

Knowledge

Measures innovation through research and patent activity.

• 80% patents (the volume, complexity, and potential of all patents created in the ecosystem)
  
• 50% log of tier of number of all the patents in the ecosystem in 2012–2021.
• 30% three-year moving average growth of all patents.
• 10% 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) in 2012–2021.
  
• 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 all research at the country level in 1996–2021.
  

Emerging Ecosystems Ranking

Emerging ecosystems are those ecosystems following the top 40 global ecosystems in the Performance Success Factor. The factor weights used to rank these ecosystems are slightly different from those used with top ecosystems to reflect their emerging status and emphasize the factors that have more influence in ecosystems that are just beginning to grow. Less weight is given to the number of exits over $50 million and startup activity is more focused on early-stage funding than in the top 40 ecosystems.

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

• Performance: 40%
• Funding: 30%
• Market Reach: 12.5%
• Talent & Experience: 12.5%
• Connectedness: 2.5%
• Knowledge: 2.5%

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 2020–2022 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 2020–2022.
• 20% Exit Growth Index (scored from 1 to 10) for 2019–2020 vs. 2021–2022.
  
• 10% Startup Success
• 80% growth-stage success (50% ratio of Series C-to-A startups and 50% log of unicorns from H2 2020–2022)
• 10% speed to exit (50% average company age at exit and 50% average company age at IPO) from H2 2020–2022.
• 10% early-stage success (ratio of Series B to Series A startups) from H2 2020–2022.
  

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). The time range for seed rounds is July 1, 2019 to December 31, 2021 and for Series A rounds is July 1, 2020 to Dec 31, 2022.
• 10% log of early-stage funding growth in 2019–2020 vs. 2021–2022
  

Market Reach

Measures early-stage startup access to customers allowing them to scale and “go-global.”

• 80% Globally Leading Companies
  
• 45% ratio of startups valued at $1 billion+ to GDP (in billions) from H2 2020–2022.
• 25% ratio of $50 million+ exits by Metro population (in millions) from H2 2020–2022.
• 10% log of ratio of $50 million+ exits in H2 2020–2022 to Series A funding in H2 2020–2022
• 20% proportion of tech startups (formed after 2012) with one or more international secondary offices.
• 20% Local Market Reach
  
• Log of country GDP.
  

Talent & Experience

Assesses the talent early-stage startups have access to and the degree of startup experience in an ecosystem.

• 50% Talent
  
• 80% Tech Talent
• 50% Quality & Access
• 70% log of count of $50 million+ exits from 2013–2022.
• 10% share of top Github coders to total Github coders
• 20% log of count of Github coders with more than 10 followers on github.com.
• 50% Cost
  
• 50% log of software engineer salary — lower is better — from Glassdoor, Salary.com, and PayScale
  
• 50% log of funding runway: The ratio of median Series A funding rounds by software engineer salary.
  
• 20% STEM Students: log of number of STEM students.
  
• 50% Experience
  
• 80% Startup Experience in Ecosystem
  
• Log of count of Series A funding in 2013–2022.
  
• 20% Scaling Experience in Ecosystem (the cumulative number of significant $50 million+ and $1 billion+ exits over 10 years for startups founded in the ecosystem).
  
• 60% log of number of $1 billion+ exits in 2013–2022.
• 40% log of number of $50 million+ exits in 2013–2022.

Connectedness

Measures how connected the ecosystem is to the global fabric of knowledge within the ecosystem.

• 80% Local Connectedness
• 70% log of count of Meetup groups on meetup.com.
• 20% log of ratio of number of Meetup groups from meetup.com by population (M).
• 10% log of the number of accelerators and incubators.
• 20% Global Connectedness
• 90% log of tech companies with secondary offices in the ecosystem.
• 10% log of international investors.
  

Knowledge

Measures innovation through research and patent activity.

• 80% Patents (the volume, complexity, and potential of all patents created in the ecosystem).
  
• 50% log of tier of number of all the patents in the ecosystem in 2012–2021.
• 30% three-year moving average growth of all patents.
• 10% 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) in 2012–2021.
• 10% complexity score of patents, a measure of the capacity of the ecosystem for producing 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 all research at the country level in 1996–2021).
  

Strong Starters Ranking

The Strong Starters ranking identifies the top 25 ecosystems from the Emerging Ecosystems ranking where early-stage funding activity is most robust.

Funding

• 90% Early-stage Funding:
• 80% log of count of early-stage funding deals. The time range for seed rounds is July 1, 2019 to December 31, 2021 and for Series A rounds is July 1, 2020 to Dec 31, 2022.
• 20% log of the sum of total early-stage funding deals. The time range for seed rounds is July 1, 2019 to December 31, 2021 and for Series A rounds is July 1, 2020 to December 31, 2022.
  
• 10% Log of Early-stage Funding Growth from 2019–2020 vs. 2021–2022.
  

Changes in Ecosystem Value

It is our constant endeavor to improve our quality of research and data, in order to help our members and readers gain accurate and current knowledge on global startup ecosystems. With that aim in mind, we have significantly improved our data set since the GSER 2021 — 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. For rounds later than Series B, we also include companies with formation dates since 1995 in our dataset.
   
3. Increasing unicorns data: We have made enormous strides in expanding unicorn coverage in our dataset. This includes incorporating CB Insights unicorns and $1 billion+ exits (after in-depth checks). This contributed to an approximate 36% increase in Ecosystem Value of the top ecosystems.
   
4. Fine-combing through big deals: As a final check, we scrupulously examined the larger deals of each ecosystem to make sure that every 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 the H2 2019. These large exits stay in their ecosystem, mostly in the form of dry powder for investors to expand their portfolios.
   
6. We have also updated the exit type. From this year onwards, we are including buyouts and reverse mergers as relevant exit deal types.
   
7. 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.