TOP 10 Countries by STEM Graduates per 100,000 Population (2025)
STEM graduates per 100,000 people: a compact way to compare national talent pipelines (2025 snapshot)
Counting total STEM graduates can mislead because country size dominates the picture. A per-capita view answers a cleaner question: how many science, technology, engineering, mathematics and computing graduates does a country produce each year relative to its population?
This indicator captures graduation intensity, not educational quality or labour-market fit. High graduation density can coexist with limited domestic R&D absorption, skills mismatch, or outward migration.
Top 10 countries by STEM graduates per 100,000 population (2025)
The leaders combine two different models: (1) advanced industrial economies with mature R&D systems and strong technical universities, and (2) emerging systems that produce very large STEM cohorts but often invest less in R&D relative to GDP. The numbers below are rounded to show the order of magnitude.
Very high STEM density for its size; a large STEM share in the graduation mix, but R&D intensity is modest.
Large STEM cohorts and a STEM-heavy mix; converting that supply into high-productivity jobs depends on domestic innovation demand.
High STEM density paired with very high R&D spending, consistent with a technology-led growth model.
A deep engineering base aligned with high-tech manufacturing and strong industry–research links.
Large technical university and engineering school ecosystem with steady R&D capacity.
High graduate density in a diversified tertiary system; outcomes depend strongly on innovation demand and sectoral structure.
A fast-converging European economy with growing technical cohorts and expanding R&D effort from a lower base.
STEM-heavy graduation profile; strengthening research and knowledge-intensive jobs is key for domestic absorption.
Strong STEM share in the graduation mix; innovation outcomes depend on R&D funding and enterprise demand.
High graduate density with a more diversified graduation mix; performance depends on matching skills to high-productivity sectors.
Table 1. STEM graduates per 100,000 population (Top 10), 2025
| Rank | Country | STEM graduates per 100,000 population |
|---|---|---|
| 1 | Tunisia | ≈ 950 |
| 2 | Iran | ≈ 890 |
| 3 | Korea (Rep.) | ≈ 780 |
| 4 | Germany | ≈ 610 |
| 5 | France | ≈ 580 |
| 6 | United Kingdom | ≈ 560 |
| 7 | Poland | ≈ 540 |
| 8 | Türkiye | ≈ 520 |
| 9 | Russia | ≈ 510 |
| 10 | Spain | ≈ 480 |
Values are rounded and intended to reflect relative scale, not exact headcounts. The key comparison is the density of STEM graduation in the population.
Table 2. STEM as a share of all tertiary graduates and R&D intensity (selected), 2025
| Country | STEM graduates, % of all graduates | R&D expenditure, % of GDP (approx.) |
|---|---|---|
| Tunisia | ≈ 40% | ≈ 0.6% |
| Iran | ≈ 38% | ≈ 0.8% |
| Korea (Rep.) | ≈ 34% | ≈ 4.7% |
| Germany | ≈ 30% | ≈ 3.1% |
| France | ≈ 29% | ≈ 2.3% |
| United Kingdom | ≈ 27% | ≈ 1.8% |
| Poland | ≈ 32% | ≈ 1.4% |
| Türkiye | ≈ 31% | ≈ 1.1% |
| Russia | ≈ 35% | ≈ 1.0% |
| Spain | ≈ 25% | ≈ 1.4% |
A STEM-heavy graduation mix can be a strength, but without sufficient R&D and knowledge-intensive jobs it can translate into underemployment, mismatch, or outward migration.
Chart 1. STEM graduates per 100,000 population (Top 10), 2025
Differences of a few hundred graduates per 100,000 people can represent large shifts in a country’s annual technical talent supply, especially for mid-sized economies.
Chart fallback (data shown as bars)
Units: graduates per 100,000 population (rounded). Data timing: mostly 2023–2024 releases used as a 2025 snapshot proxy.
Methodology (how the ranking is built)
The ranking uses tertiary education statistics by field of study from international and national sources, focusing on graduates in STEM-aligned fields (natural sciences; engineering, manufacturing & construction; ICT; mathematics and statistics). To standardise across country size, the annual number of STEM graduates is divided by total population and expressed per 100,000 inhabitants. The “2025” label reflects a practical publication convention: the latest harmonised releases commonly lag by one to two years, so values around 2023–2024 are used as the closest comparable proxy for a 2025 snapshot.
For cross-country readability, values are rounded to emphasize magnitude and ordering rather than small differences. Where multiple sources exist, series are reconciled by aligning field definitions and tertiary coverage, prioritising harmonised international frameworks and the most recent consistent release. Key limitations include differences in programme classification, varying graduation cycles, and reporting lags. The metric also does not capture the quality of training, actual skills, or whether degrees translate into STEM employment.
Insights (what patterns stand out)
Three broad patterns tend to emerge when looking at STEM graduate density together with the STEM share of graduates and R&D intensity. First, some emerging systems produce exceptionally large STEM cohorts relative to population size, often reflecting policy-driven expansion of technical degrees. Second, advanced industrial economies show consistently high graduation density alongside high R&D spending, indicating a deeper domestic demand for technical skills in research, engineering, and knowledge-intensive industry. Third, a STEM-heavy graduation mix without commensurate R&D and high-productivity job creation can create a “pipeline pressure” where graduates flow into non-STEM jobs or consider emigration in search of better matching opportunities.
What this means for readers
If you are comparing countries for study, work, or investment, STEM graduates per 100,000 offers a quick signal about how “technical” a country’s education output is. High values can indicate a strong supply of engineers and developers, which can support industrial upgrading, green transition capacity, and technology adoption. At the same time, a strong supply does not guarantee strong outcomes: career prospects depend on the availability of R&D-intensive employers, start-up ecosystems, and the broader demand for advanced skills. Pairing this ranking with R&D spending (and, ideally, productivity and labour-market indicators) gives a more complete picture.
FAQ
Why can a smaller country rank higher than a very large one?
The ranking is per-capita. A country with a strong technical university system can produce a very large STEM cohort relative to its population even if its total number of graduates is smaller than in large countries.
Does this measure the quality of STEM education?
No. It measures the density of graduates. Quality, research excellence, and job readiness require additional indicators (outcomes, assessments, employer data, and R&D ecosystem measures).
Why do Tunisia and Iran appear so high in the per-capita ranking?
Very high per-capita graduation can reflect national policy emphasis on technical degrees and the structure of tertiary enrolment. Whether those graduates are fully absorbed depends on domestic innovation demand and the availability of high-skill jobs.
Is “STEM share of graduates” the same as “STEM graduates per 100,000”?
They answer different questions. “Per 100,000” captures how many STEM graduates are produced relative to population. “STEM share” captures how STEM-oriented the overall graduation mix is, regardless of system size.
Why combine STEM graduation metrics with R&D spending?
R&D spending (as a share of GDP) is a rough proxy for innovation demand. Countries combining high STEM output with high R&D intensity are more likely to have strong domestic absorption into research and high-tech roles.
What can distort comparisons across countries?
Differences in programme classification, reporting lag, tertiary system structure (short-cycle vs bachelor vs master), and graduation timing can shift values. The per-capita approach reduces scale bias but does not remove definitional differences.
Interactive view: compare STEM graduate density and connect it to R&D effort
The table below keeps the full dataset visible in HTML (no JS-generated rows). JavaScript only improves usability: search, filter, and sorting. The scatter chart then links STEM share with R&D spending.
Table 3. STEM graduates per 100,000 (Top 10) with region and income group
Default dataset shown: Top 10. Values are rounded. Region and income group are included for filtering context.
| Rank | Country | STEM graduates per 100,000 | Region |
|---|---|---|---|
| 1 | Tunisia | ≈ 950 | MENA |
| 2 | Iran | ≈ 890 | MENA |
| 3 | Korea (Rep.) | ≈ 780 | Asia |
| 4 | Germany | ≈ 610 | Europe |
| 5 | France | ≈ 580 | Europe |
| 6 | United Kingdom | ≈ 560 | Europe |
| 7 | Poland | ≈ 540 | Europe |
| 8 | Türkiye | ≈ 520 | Europe |
| 9 | Russia | ≈ 510 | Europe |
| 10 | Spain | ≈ 480 | Europe |
Data timing: mostly 2023–2024 releases used as a 2025 snapshot proxy. Source families: UNESCO/OECD/national education statistics.
Chart 2. STEM graduation mix vs R&D spending (selected countries)
Each point places a country by STEM share of all tertiary graduates (x-axis) and R&D expenditure as a share of GDP (y-axis). Countries in the top-right combine a STEM-oriented graduation mix with strong innovation demand.
Chart fallback (data table)
| Country | STEM share | R&D (% of GDP) |
|---|---|---|
| Tunisia | ≈ 40% | ≈ 0.6% |
| Iran | ≈ 38% | ≈ 0.8% |
| Korea (Rep.) | ≈ 34% | ≈ 4.7% |
| Germany | ≈ 30% | ≈ 3.1% |
| France | ≈ 29% | ≈ 2.3% |
| United Kingdom | ≈ 27% | ≈ 1.8% |
| Poland | ≈ 32% | ≈ 1.4% |
| Türkiye | ≈ 31% | ≈ 1.1% |
| Russia | ≈ 35% | ≈ 1.0% |
| Spain | ≈ 25% | ≈ 1.4% |
Interpretation guide: bottom-right indicates a STEM-heavy graduation profile with comparatively modest R&D absorption capacity.
How to interpret a “Top 10” STEM graduation ranking (and what it does not say)
A high number of STEM graduates per 100,000 people is a strong signal that a country is producing a dense pipeline of technically trained graduates. It often correlates with the capacity to scale engineering-intensive sectors and to adopt new technologies faster. However, the same figure can reflect very different realities: some countries pair large STEM cohorts with deep innovation demand, while others face weaker absorption into R&D and high-productivity jobs.
The most informative reading combines three layers: (1) STEM graduates per 100,000 (talent supply intensity), (2) STEM share of all graduates (how STEM-oriented the system is), and (3) R&D spending (a proxy for domestic innovation demand).
What the 2025 snapshot suggests
- Two-track leadership. The top of the ranking includes advanced industrial economies with strong R&D systems, but also emerging countries producing very large STEM cohorts relative to population size.
- Supply vs absorption. A STEM-heavy graduation mix can be a competitive advantage only if graduates can move into productive roles in engineering, data, research, and innovation-driven firms.
- Migration pressure in some cases. Where local demand is limited, high STEM graduation density can coincide with outward mobility of talent to higher-wage innovation hubs.
- Industrial structure matters. Countries with advanced manufacturing, scaled ICT services, and strong research–industry links typically convert STEM output into higher productivity more reliably.
Policy takeaways
- Match STEM expansion with innovation demand. If the graduation pipeline rises, R&D incentives, tech diffusion policies, and support for high-productivity sectors help prevent mismatch.
- Strengthen pathways from degree to job. Internships, applied research programmes, and employer partnerships improve conversion of graduates into STEM employment.
- Balance the portfolio. STEM output is critical, but innovation systems also require management, design, policy, and social capabilities to translate technology into scaled adoption.
- Use multiple indicators. Graduation density is an input metric; pair it with labour-market outcomes (STEM employment, wages), R&D intensity, patenting/innovation measures, and productivity indicators.
Sources (primary datasets and documentation)
The ranking concept draws on tertiary graduates by field of education and population normalisation, commonly assembled from the sources below. For country-level verification and methodological details, use the official documentation alongside the data series.
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UNESCO Institute for Statistics (UIS) — education statistics by field of studyhttps://uis.unesco.org/
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OECD — Education indicators and graduates by fieldhttps://www.oecd.org/education/
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OECD — Main Science and Technology Indicators (MSTI) and R&D related datasetshttps://www.oecd.org/sti/msti.htm
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World Bank Data — population and R&D expenditure (% of GDP)https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS
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National education ministries and statistical offices (country updates, classification notes)https://unstats.un.org/unsd/methodology/m49/
Update convention: “2025” is used as a publication snapshot label; the most recent harmonised releases commonly refer to 2023–2024.
