Methodology

Our quantitative data infrastructure is the world’s most comprehensive and quality controlled.
We study more than 5 million companies across 350+ 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. We work with 65+ countries to power and update the data found in our reports and policy consultancy work.

Key Definitions

Ecosystem: We define a startup ecosystem as a shared pool of resources, generally located within a 62 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.

Exit: An exit, in the context of startups, refers to an event in which the founders, investors, or employees of a startup realize a return on their investment by selling their ownership stake in the company. Exits include IPOs, M&A, buyouts, and reverse mergers. We only include the first exit as relevant.

H1/H2: Fiscal periods of half a year, in which January–June is H1 and July–December is H2. Similarly, Q1, Q2, etc. refers to the four fiscal quarters of a year (January–March, April–June, etc.).

Regions: We define global regions based on UN and World Bank definitions and divide all countries into seven regions: Asia, Europe, Latin America, MENA, North America, Oceania, sub-Saharan Africa. For a full list of which ecosystems are included in each region, please see here.

Startup: We define a startup as an innovative or technology-driven company that was founded within the last 10 years and that has technology and/or scalability at the core of its business model. In addition to software, this includes startups active in Deep Tech, such as Robotics, Life Sciences, and more.

Unicorn: A startup that meets our definition, has been valued at more than $1 billion, and has not exited.

Sub-Sector Definitions

Sub-sectors are not mutually exclusive nor comprehensive — some startups are in sub-sectors that we do not consider. In addition, we are aware of a clear tech convergence. Technologies such as AI software are increasingly interrelated, and we would expect a similar convergence over time for other startup sub-sectors.

Advertising Tech (Adtech): 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 (AMR): The use of smart technology to improve traditional manufacturing of products and/or processes, and 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.

Artificial Intelligence, Big Data & Analytics (AI & BD): 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: A decentralized data storage method secured by cryptography. Companies building their product/architecture on top of this decentralized and encrypted technology are defined as Blockchain companies. Cryptocurrencies are one of many innovations utilizing Blockchain.

Cleantech: 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.

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

Edtech: Technology devoted to the development and application of tools (including software, hardware, and processes) intended to redesign traditional products and services in education.

Fintech: Technology that aims to improve existing processes, products, and services in the Financial Services industry (including insurance).

Gaming: The development, marketing, and monetization of video games and gambling machines, as well as associated services.

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

Ecosystem Page Metrics

Ecosystem Value: A measure of economic impact, calculated as the value of exits and startup valuations from H2 2022–2024. Ecosystem value includes all active unicorns.

Ecosystem Value growth (CAGR): CAGR is calculated based on companies funded in the ecosystem in H2 2020–2022 vs. H2 2022–2024.

Total Early-Stage Funding: The total seed and Series A funding in tech startups in H2 2022–2024.

Total VC Funding: The total VC funding (seed, Series A, Series B+) in tech startups in 2020–2024.

Median Series A: The median of Series A rounds in tech startups in the ecosystem in H2 2022–2024.

Median Seed: The median of seed rounds in tech startups in the ecosystem in H2 2022–2024.

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 2020–2024.

For additional definitions, please see Startup Genome’s Glossary.

Primary Data Sources

Startup Genome proprietary data:
- Interviews of 100+ experts
- 2017–2024 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 (2017-2024). StartupGenome.com database
Dealroom.co BV. (2017-2024). Dealroom.co database
Crunchbase (2017-2024). Crunchbase.com database
CB Insights (2019-2024). Cbinsights.com database
Orb Intelligence Inc. (2017-2024). orb-intelligence.com database
PitchBook (2018-2024), a private capital market data provider
Tracxn

Secondary Data Sources

Forbes 2000
GitHub API
International IP Index
Meetup.com
OECD, R&D Spending
Other sources from Life Sciences Rankings
Salary 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 2022–2024.
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 2021 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 2021–2023 for seed rounds and H2 2022–2024 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 2025 (Top 40)

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: 29%
Funding: 24%
AI-Native: 2%
Market Reach: 20%
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 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 startup valuations during H2 2022–2024 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 2022–2024
- 20% exit growth index (scored from 1 to 10) from 2021–2022 vs. 2023–2024
12.5% Startup Success
- 80% growth-stage success (50% ratio of Series C-to-A Startups and 50% from number of unicorns, companies over $B valuation) from H2 2022–2024
- 20% early-stage success (ratio of Series B-to-A startups) from H2 2022–2024

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 H2 2021–2023 and for Series A rounds is H2 2022–2024.
- 10% log of early-stage funding growth from Seed count from H2 2020–2022 and Series A Count from H2 2021–2023 to Seed count from H2 2021–2023 and Series A Count from H2 2022–2024
10% Quality and Activity
- 70% volume of investors (50% log of total number of VCs and CVCs in 2024 and 50% log of total number of investors with $100 million+ assets under management in 2024)
- 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

AI-Native Transition
This factor is a composite measure of the degree to which an ecosystem encourages artificial intelligence (AI) startups. This sector has been highlighted over others since Startup Genome believes that AI is increasingly a general purpose technology which will drive growth in other sectors.

100% AI-Native

50% ratio of AI & Big Data startups to all technology startups formed in 2023–2024
40% ratio of AI-Native startups to all technology startups formed in 2023–2024
10% ratio of AI-Native total VC funding to all technology total VC funding in 2023–2024

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

75% Local Reach
- 60% Scaleup Production
  - 50% ratio of startups with $1 billion+ valuations to GDP from H2 2022–2024
  - 40% ratio of $50 million+ exits to GDP from H2 2022–2024
  - 10% log of ratio of exits over $50 million from H2 2022–2024 to Series A funding from H2 2022–2024
- 40% Local Market
  - 90% from the log of GDP of the country
  - 10% from tiers of average number of days to commercialization of IP assets
25% Global Reach
- 60% ratio of tech startups (formed after 2015) with international secondary offices
- 20% from the log of tech companies with secondary offices in the ecosystem
- 20% from the 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 2015–2024
    - 10% share of top Github coders to total Github coders (based on the data available in December 2024)
    - 10% log of count of Github coders on github.com with more than 10 followers (based on the data available in December 2024)
    - 10% English Proficiency Score for 2024
- 10% Cost
  - 50% log of software engineer salary — lower is considered better — from Glassdoor, Salary.com, and PayScale for 2024
  - 50% log of funding runway: ratio of median Series A funding rounds for H2 2022–2024 by software engineer salary
- 20% Life Sciences
  - 30% STEM students: log of number of STEM students
  - 60% 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 World-Class Faculty 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 2015–2024
- 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 in 2015–2024
  - 40% log of number of $50 million+exits in 2015–2024

Knowledge

Measures innovation through research and patent activity.

100% patents (the volume, complexity, and potential of all patents created in the ecosystem)
- 60% log of tier of number of all the patents in the ecosystem in 2014–2023
- 20% five-year moving average growth of all patents
- 20% technology potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces

Emerging Ecosystems Ranking

Emerging ecosystems are startup communities at earlier stages of growth. The methodology for ranking the Top 100 Emerging Ecosystems is designed to reflect this, showcasing the ecosystems displaying high potential to become top global performers in the coming years. The factor weights used to rank these ecosystems differ slightly from those used with the 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: 30%
Funding: 32.5%
Market Reach: 15%
Talent & Experience: 17.5%
Knowledge: 5%

Performance

Captures the actual leading, current, and lagging indicators of ecosystem performance.

75% Ecosystem Value
- Log of sum of all exits and estimated startups valuations during H2 2022–2024 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 2022–2024
- 20% Exit Growth Index (scored from 1 to 10) for 2021–2022 vs. 2023–2024
5% Startup Success
- 80% growth-stage success (50% ratio of Series C-to-A startups and 50% log of unicorns from H2 2022–2024)

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 H2 2021–2023 and for Series A rounds is H2 2022–2024
- 10% log of early-stage funding growth from Seed count from H2 2020–2022 and Series A Count from H2 2021–2023 to Seed count from H2 2021–2023 and Series A Count from H2 2022–2024

Market Reach

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

80% Local Reach
- 62.5% Scaleup Production
  - 55% ratio of startups with $1 billion+ valuations to GDP in H2 2022–2024
  - 45% ratio of $50 million+ exits to GDP in H2 2022–2024
- 37.5% Local Market
  - 100% from the log of GDP of the country
20% Global Reach
- 80% ratio of tech startups (formed after 2015) with international secondary offices
- 20% from the log of tech companies with secondary offices in the ecosystem

Talent & Experience

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

43% Talent
- 80% Tech Talent
  - 50% Quality & Access
    - 50% log of count of $50 million+ exits from 2015–2024
    - 30% 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 considered 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
57% Experience
- 80% Startup Experience in Ecosystem
  - Log of count of Series A funding in 2015–2024
- 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 the number of $1 billion+ exits in 2015–2024
  - 40% log of the number of $50 million+ exits in 2015–2024

Knowledge

Measures innovation through research and patent activity.

100% patents (the volume, complexity, and potential of all patents created in the ecosystem)
- 60% log of tier of number of all the patents in the ecosystem in 2014–2023
- 20% three-year moving average growth of all patents
- 20% technology potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces

Changes from GSER 2024

Startup Genome continuously aims to improve its data and research. Therefore, as with previous years, we have made a number of changes to our methodology to reflect our changing understanding, as well as our beliefs concerning the reliability and representativeness of data sources.

This year we added the “AI-Native Transition” factor, a composite measure of the degree to which an ecosystem encourages artificial intelligence (AI) startups. This sector has been highlighted over others since Startup Genome believes that AI is increasingly a general purpose technology that will drive growth in other sectors.

Previous editions included a handful of small countries along with city-level ecosystems. This year, we have shifted entirely to city-level ecosystems, using our standard definition of a 100 km radius. We will continue to cover countries in our APEXE Nations Report.

The second major change in our rankings is that we now include all unicorns in our calculations, regardless of whether they have had a deal in the past 2.5 years. Previously, we only considered unicorns with deals in the last 2.5 years, but this year, we are including all unicorns that are still operational.

Finally, we changed our Knowledge factor to remove the H-index, as this metric was on a country level. We are still identifying sources that provide this information on an ecosystem level.

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:

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.
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.
Increasing unicorns data: we have made enormous strides in expanding unicorn coverage in our dataset. This includes incorporating CB Insights on unicorns and $1 billion+ exits (after in-depth checks). This contributed to an approximate 36% increase in Ecosystem Value of the top ecosystems.
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.
In previous years, we have only considered the ecosystem that a startup is founded in. From last 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.
From this year onwards, we are including all active unicorns in our calculation.
Also from this year onwards, we are excluding billion dollar exits which are from the previous GSER time range and not present in the current GSER time range.