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What counts as an innovation cluster?
An innovation cluster is a concentration of connected firms, researchers, investors, workers, and support institutions. Proximity can make it easier to share specialized knowledge, find skilled workers, access infrastructure, and connect research with business needs. OECD research describes clusters as firms and research laboratories located close to one another, with their mix and specialization shaped by sector and history: OECD, Cities and Regions in the New Learning Economy.
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The cluster may extend beyond a city’s municipal boundary. WIPO’s Global Innovation Index identifies clusters from the locations of inventors named in patent applications, scientific article authors, and firms receiving venture-capital investment; its bottom-up approach does not assume that a cluster matches a political boundary: WIPO, Global Innovation Index 2026. Depending on the question and available data, the relevant area might be a city proper, metropolitan region, commuting zone, or data-defined cluster.
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Separate inputs, activity, implementation, and outcomes
A useful scorecard distinguishes what a place has from what it does and what results follow. The OECD’s ecosystem diagnostics keep inputs and outputs separate because ecosystem elements interact, and a weakness in one area can constrain the whole system: OECD, Entrepreneurial Ecosystem Diagnostics.
#1 Best Overall
- Inputs: talent, research capacity, finance, institutions, infrastructure, markets, leadership, and business support.
- Activities and intermediate signals: research publications, patenting, startup formation, collaboration, and investment.
- Implemented innovation: products or processes that are actually made available or put into use.
- Outcomes: firm survival and growth, jobs, productivity, and wider social or economic effects.
The distinction matters because invention is not the same as innovation. OECD and Eurostat’s Oslo Manual 2018 defines innovation as: “An innovation is a new or improved product or process (or combination thereof) that differs significantly from the unit’s previous products or processes and that has been made available to potential users (product) or brought into use by the unit (process).” The manual cautions against treating R&D inputs or patented inventions as a full measure of innovation: OECD/Eurostat, Oslo Manual 2018, 4th Edition.
Which metrics help identify a cluster?
Use multiple measures, and interpret each in relation to the others. WIPO’s approach combines three geographically located signals—patent inventors, scientific authors, and firms receiving venture-capital investment—rather than relying on one count. The OECD ecosystem framework adds measures of enabling conditions and entrepreneurial outputs.
Rank #2
Invention and research
- Inventors on published patent applications: Locate inventors consistently and specify whether applications or granted patents are counted. Patent activity can signal inventive work, but patents represent only some inventions and do not show whether an invention was put into use.
- Scientific authors and publications: Geographically located authors can show where research activity is concentrated. Explain how co-authored work is counted; fractional counting, where each contributor receives a share, can affect comparisons.
- R&D spending and research employment: These describe knowledge inputs and research capacity. They do not, by themselves, show that research became a useful product, process, or broader benefit.
Finance and entrepreneurship
- Venture-capital activity: Track both where VC-backed firms are located and the scale of investment. WIPO uses locations of firms receiving VC investment as a cluster signal; the OECD framework includes early- and later-stage VC investment per capita among finance indicators.
- Business formation: Employer-startup birth rates and firm creation help show whether ideas are becoming operating businesses. Interpret rates against the relevant population or business base.
- Survival, growth, and employment: Track whether firms remain active, expand, and create jobs. OECD’s concept of productive entrepreneurship includes job creation, innovation, survival, and growth potential—not only a small number of celebrated high-growth companies.
Funding totals show capital flows, not the quality or breadth of innovation. Compare them with population, industry mix, firm formation, and business outcomes before drawing conclusions.
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The OECD diagnostic framework identifies ten ecosystem inputs: institutions, culture, networks, infrastructure, markets, finance, knowledge, talent, leadership, and intermediate services. Its example indicators include education, technical employment, R&D, patents, SME innovation collaboration, university-business collaboration, broadband and transport infrastructure, serial entrepreneurs, coaches, and incubators.
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Also look for the mechanisms that connect these inputs: skilled labor pools, shared infrastructure, specialist services, and links between firms and research institutions. OECD’s city analysis notes that clusters may have an anchor company, public laboratory, or large university. Stanford University is one example associated with Silicon Valley; CEA is an example for Grenoble. These anchors can help explain a cluster’s development, but the presence of an institution alone does not prove that a cluster is thriving.
How to compare cities fairly
Before comparing scores, state what area is being measured and how. A city-proper figure for one place is not directly comparable to a metropolitan-area figure for another. WIPO’s boundary approach illustrates why administrative borders may not capture the functional geography of innovation. OECD also cautions that national-level indicators and composite indices do not provide a complete picture of a local ecosystem.
- Define the unit: Say whether the measure covers the city proper, metropolitan area, commuting zone, or a data-defined cluster.
- Disclose the denominator: Identify whether rates are per resident, worker, firm, or another population. Absolute totals tend to favor larger places.
- Give the reference year and time window: Patent, publication, funding, and firm data may refer to different periods. Rankings can use data years that differ from the publication year.
- Use a balanced set of indicators: Compare activity, translation into businesses or products, talent, ecosystem conditions, and outcomes rather than relying on one headline measure.
- Describe coverage and counting rules: Note data gaps, geocoding, treatment of co-authors or inventors, and any fractional-counting method.
- Check who benefits: Citywide averages can hide regional and social differences in access to entrepreneurship and its outcomes.
| Dimension | Useful indicators | What the indicators can show | What they cannot establish alone |
|---|---|---|---|
| Innovation activity | Inventor and author concentration; patenting; research output | Where inventive and research work is concentrated | Whether ideas were implemented or benefits were widely shared |
| Translation and finance | VC activity; firm formation; university-business links; product and process adoption | Whether knowledge connects with financing and business use | Whether funding totals represent broad or durable innovation |
| Talent and capability | Education and training; skilled-worker availability; technical employment | Whether the area has relevant skills and capacity | Whether workers can access opportunities or firms can retain them |
| Ecosystem conditions | Networks; infrastructure; institutions; markets; leadership; specialist services | Whether enabling connections and support are present | Whether any one institution or condition guarantees cluster success |
| Outputs and reach | Employer-firm births; survival and growth; jobs; distribution of outcomes | Whether entrepreneurial activity produces durable economic and social results | Every broader effect of innovation or how evenly benefits are distributed |
Why common headline measures can mislead
- Patent counts are not innovation counts. Patents capture only part of inventive activity, and the Oslo Manual’s definition requires implementation or availability to users.
- R&D is an input. High research spending does not show whether knowledge became useful products, processes, or outcomes.
- VC totals are incomplete. They measure investment activity, not the full range of financing, business innovation, or social value.
- One ranking is not a full diagnosis. Geocoding, boundaries, time windows, population grids, and chosen indicators can change which clusters are identified and how they compare.
- Science and technology measures miss some innovation. OECD regional policy analysis warns that R&D- and science-based measures do not capture the entire spectrum of innovation: OECD, Innovation and Regional Development.
- City averages obscure distribution. A strong aggregate result can coexist with unequal access to skilled jobs, finance, or business opportunities.
Clusters can specialize in different ways
There is no single template for a successful innovation cluster. OECD’s city analysis discusses sector-specific examples including aeronautics in Toulouse, finance in London and Frankfurt, and automotive activity in Stuttgart. These examples show that clusters reflect different histories and specializations; a technology-startup profile is not the only possible form of innovation concentration.
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