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PIPE IP Commercialisation Index · 2024/25 Editionv3.6 BETA

University R&D
Commercialisation
Performance

346 universities across 13 countries ranked by a composite impact index spanning patent productivity, spinout formation, IP revenue, TTO efficiency, and the hidden cost of unrealised commercialisation potential.

346Institutions ranked
13 countries🇬🇧 🇦🇺 🇳🇿 🇺🇸 🇧🇪 🇨🇦 🇫🇷 🇩🇪 🇮🇹 🇳🇱 🇵🇹 🇪🇸 🇸🇪
HE-BCI 2023/24Primary UK source
IPEDS/AUTMPrimary US source

ⓘ Impact Data & Scoring

The underlying data powering this index can be viewed and amended in the Impact Data Simulator. Full scoring methodology: Methodology Note →. This table will update from changes made within the Impact Data Simulator.

Data caveats. Friction scores are model-derived estimates calibrated against PraxisAuril Knowledge Exchange benchmarks and AUTM sector data. They are comparative measures between institutions, not independently observed figures, and should be interpreted accordingly. Friction figures also reflect the disclosure gap: research suggests fewer than half of commercially patentable inventions are ever disclosed to a TTO, meaning a portion of model friction represents ideas that bypassed the TTO entirely, not TTO capacity failure. See the Methodology Note for full detail. Total revenue figures exclude endowment income and consultancy receipts; for institutions with substantial endowments or commercial research programmes, notably Oxford and Cambridge — actual income will be higher than the model reflects. The index does not differentiate between the quality or commercial performance of individual spinout companies, only their formation rate and three-year survival. Model-derived figures (IP revenue, IP yield ratio, IP to staff cost) are internally consistent calibration outputs and should not be compared directly to HESA HE-BCI reported figures; they are calibrated for relative ranking within peer groups.

ⓘ How the Impact Score is calculated

Each university is scored across six dimensions, normalised within its currency peer group. All dimensions carry equal weight.

Dimension Variables Rationale
IP utilisation (lic + spin) ÷ pat × 100 (Licences issued + spinouts formed) ÷ patents × 100. Measures how effectively each patent in the portfolio generates a commercial outcome. A patent underpinning neither a licence nor a spinout is a cost without return. Values are capped at 100 in scoring.
Spinout formation spinRate New ventures formed per 100 senior academic staff per year. Normalised to remove scale bias between institutions.
Spinout survival spinSurv Percentage of spinout companies still operating after three years. Separates durable ventures from those formed for optics. UK sector average approximately 55%.
IP revenue ipRevAdj = (spinRate × Texp/100 × spinSurv × spinIncome) + (patents × licRate × licIncome/1000)
Total income from surviving spinouts and licence agreements in local currency. Not cross-currency comparable, scored within currency peer group only. Note: patents appear in this formula as the mechanism for deriving annual licence volume (patents × licRate); they are not a scored output. IP utilisation measures whether those patents generate commercial outcomes.
IP yield ratio ipRevAdj ÷ opCostTTO IP income relative to TTO operating cost. A ratio above 1× means the TTO generates more than it costs. 32 of 140 UK universities currently achieve this.
IP to staff cost ipRevAdj ÷ researchCost × 100 IP income as a percentage of research-attributed staff cost (senior staff × research effort + 20% of junior staff + all postdocs), the closest public equivalent to ROCE for university commercialisation. Values above 100% are valid: MIT recovers 492% of its research staff cost through IP income. UK median ~1%. EUR-group values are estimated.
Impact Data Simulator
Score = weighted composite, normalised within country group ⚠ Friction metric is inverted, lower waste scores higher ⚠ Values in local currency per country group, not directly comparable across borders
Country:
Apr 2025 · v3.6 BETA
# University Impact score↓?Composite score across 6 dimensions: IP utilisation (licences+spinouts per patent), spinout rate, IP revenue, IP yield ratio, IP to staff cost, and friction. Normalised within currency peer group. Higher is better. IP utilisation↕?(Licences issued + spinouts formed) as a percentage of patents held: (licences + spinouts) ÷ patents × 100. Measures how effectively the patent portfolio generates commercial outcomes. A patent that generates neither a licence nor underpins a spinout represents sunk cost. Values above 100% indicate institutions generating more commercial outcomes than patents — capped at 100 in scoring. Higher is better. Spinouts/100 sr.↕?Spinout companies formed per 100 senior academic staff per year. Measures the institution's ability to convert research into new ventures. Higher is better. 3yr survival ↕?Percentage of new spinout companies still operating after 3 years. Separates institutions that form durable ventures from those that form spinouts for optics. UK sector average ~55%. IP revenue↕?Total IP income in local currency (£m / $m / €m etc). Combines surviving spinout income and licence revenue. Values are not cross-currency comparable, each country group is normalised separately. IP yield ratio↕?IP revenue divided by TTO operating cost. Measures return on the commercialisation function itself. A ratio of 2× means the TTO generates twice its own cost in IP income. Higher is better. IP to staff cost↕?IP income (spinout revenue + licence income) as a percentage of total research staff cost. A score of 100% means IP activities fully recover the institution's research payroll. Above 100% is valid and significant — MIT (492%), Stanford (450%) and Harvard (263%) each generate more in IP income than they spend on research staff. The UK median is ~1%. Values for Belgium, France, Italy, Portugal, Spain and Sweden are estimated from TTO cost ratios and should be read directionally. The closest publicly available equivalent to ROCE for university research commercialisation. Friction/mo↕?Good ideas generated per month that are never commercialised. Calculated as viable ideas/month minus (spinouts + licences)/12. Patents are excluded — a filed patent is a commercialisation stage, not an outcome; only spinouts and licences represent commercialisation. Lower friction = less wasted potential. This column is inverted in scoring, lower scores higher.
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Friction = good ideas/month minus (spinouts+licences)/12, higher friction = more wasted potential, penalised in score when weight > 0% Showing — of —

Patent trap

83%

of patents filed by universities are never commercialised — never licensed, never used by a spinout. An unused patent generates annual renewal cost, occupies examiner capacity, and creates a defensive thicket that raises friction for other institutions.

The friction gap

~60–90%

of good ideas generated monthly go unrealised at most institutions. Friction is the single biggest drag on commercialisation impact, and it is rarely measured.

IP yield ratio spread

10×

difference in IP yield ratio across this cohort. High-patent, low-realisation institutions consistently cluster at the bottom when friction is weighted.

Data sources: UK: HESA · HE-BCI 2023/24 · PraxisAuril KEB   AU/NZ: HERDC 2023 · TEC 2023   US: IPEDS FY2023 · AUTM FY2023
AU / NZ / US IP revenue in local currency. Export CSV mirrors import format for round-trip editing.