2023
Statistics
Research Spending As A Share Of Gdp
Total domestic expenditure on research and development (R&D) as a share of GDP. Includes spending by businesses, government, universities, and non-profits on basic research, applied research, and experimental development.
Interactive Trend
Research Spending As A Share Of Gdp
Research Spending As A Share Of Gdp
Source: EuropeVersus.com • Our World in Data
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Global
Eurozone
Non-Euro EU
Non-EU Europe
Regional Comparison
🇪🇺
Europe
2023
1.77
🇪🇺
EU-27
2022
2.02
🇺🇸
USA
2022
3.59
🇮🇳
India
2020
0.65
🇨🇳
China
2022
2.56
Historical Data
| Year | 🇩🇪 Germany | 🇫🇷 France | 🇮🇹 Italy | 🇪🇸 Spain | 🇳🇱 Netherlands | 🇧🇪 Belgium | 🇦🇹 Austria | 🇮🇪 Ireland | 🇵🇹 Portugal | 🇬🇷 Greece | 🇫🇮 Finland | 🇸🇰 Slovakia | 🇸🇮 Slovenia | 🇪🇪 Estonia | 🇱🇻 Latvia | 🇱🇹 Lithuania | 🇱🇺 Luxembourg | 🇲🇹 Malta | 🇨🇾 Cyprus | 🇭🇷 Croatia | 🇵🇱 Poland | 🇸🇪 Sweden | 🇩🇰 Denmark | 🇨🇿 Czechia | 🇭🇺 Hungary | 🇷🇴 Romania | 🇧🇬 Bulgaria | 🇨🇭 Switzerland | 🇳🇴 Norway | 🇬🇧 UK | 🇮🇸 Iceland | 🇱🇮 Liechtenstein | 🇲🇨 Monaco | 🇦🇩 Andorra | 🇸🇲 San Marino | 🇻🇦 Vatican | 🇺🇦 Ukraine | 🇧🇾 Belarus | 🇲🇩 Moldova | 🇷🇺 Russia | 🇦🇱 Albania | 🇧🇦 Bosnia & Herzegovina | 🇷🇸 Serbia | 🇲🇪 Montenegro | 🇲🇰 North Macedonia | 🇽🇰 Kosovo | 🇬🇪 Georgia | 🇦🇲 Armenia | 🇦🇿 Azerbaijan | 🇹🇷 Turkey | 🇪🇺 Europe | 🇪🇺 EU-27 | 🇪🇺 Core EU | 💶 Eurozone (Agg) | 🇪🇺 Non-€ EU (Agg) | 🌍 Non-EU Eur (Agg) | 🇺🇸 USA | 🇨🇳 China | 🇮🇳 India |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 0.3 | 0.6 | 0.2 | — | — | 0.2 | 1.0 | — | 0.4 | — | 0.3 | 0.2 | 0.2 | — | 1.8* | — | — | — | — | — | — | — | — |
| 2022 | 3.1 | 2.2 | 1.4 | 1.4 | 2.3 | 3.4 | 3.2 | 1.0 | 1.7 | 1.5 | 3.0 | 1.0 | 2.1 | 1.8 | 0.8 | 1.1 | 1.0 | 0.6 | 0.8 | 1.4 | 1.5 | 3.4 | 2.9 | 2.0 | 1.4 | 0.5 | 0.8 | — | 1.6 | — | 2.6 | — | — | — | — | — | 0.3 | 0.5 | 0.2 | 0.9 | — | 0.2 | 1.0 | — | 0.4 | — | 0.2 | 0.2 | 0.2 | 1.3 | 1.8 | 2.0 | 2.4 | 2.2 | 1.6 | 2.9* | 3.6 | 2.6 | — |
| 2021 | 3.1 | 2.2 | 1.4 | 1.4 | 2.3 | 3.4 | 3.3 | 1.1 | 1.7 | 1.5 | 3.0 | 0.9 | 2.1 | 1.8 | 0.8 | 1.1 | 1.0 | 0.7 | 0.8 | 1.2 | 1.4 | 3.4 | 2.8 | 2.0 | 1.6 | 0.5 | 0.8 | 3.3 | 1.9 | 2.9 | 2.8 | — | — | — | — | — | 0.4 | 0.5 | 0.2 | 1.0 | — | 0.2 | 1.0 | — | 0.4 | — | 0.3 | 0.2 | 0.2 | 1.4 | 1.8 | 2.0 | 2.4 | 2.2 | 1.6 | 2.9 | 3.5 | 2.4 | — |
| 2020 | 3.1 | 2.3 | 1.5 | 1.4 | 2.3 | 3.4 | 3.2 | 1.2 | 1.6 | 1.5 | 2.9 | 0.9 | 2.1 | 1.8 | 0.7 | 1.1 | 1.1 | 0.7 | 0.8 | 1.2 | 1.4 | 3.5 | 3.0 | 2.0 | 1.6 | 0.5 | 0.9 | — | 2.2 | 2.9 | 2.5 | — | — | — | — | — | 0.4 | 0.5 | 0.2 | 1.1 | — | 0.2 | 0.9 | — | 0.4 | — | 0.3 | 0.2 | 0.2 | 1.4 | 1.8 | 2.0 | 2.4 | 2.2 | 1.6 | 2.9 | 3.4 | 2.4 | 0.7 |
| 2019 | 3.2 | 2.2 | 1.5 | 1.3 | 2.2 | 3.2 | 3.1 | 1.2 | 1.4 | 1.3 | 2.8 | 0.8 | 2.0 | 1.6 | 0.6 | 1.0 | 1.2 | 0.6 | 0.7 | 1.1 | 1.3 | 3.4 | 2.9 | 1.9 | 1.5 | 0.5 | 0.8 | 3.2 | 2.1 | 2.7 | 2.3 | 5.9 | — | — | — | — | 0.4 | 0.6 | 0.2 | 1.0 | — | 0.2 | 0.9 | 0.4 | 0.4 | — | 0.3 | 0.2 | 0.2 | 1.3 | 1.7 | 2.0 | 2.4 | 2.1 | 1.5 | 2.7 | 3.2 | 2.2 | 0.7 |
| 2018 | 3.1 | 2.2 | 1.4 | 1.2 | 2.1 | 2.9 | 3.1 | 1.1 | 1.4 | 1.2 | 2.8 | 0.8 | 2.0 | 1.4 | 0.6 | 0.9 | 1.2 | 0.6 | 0.6 | 0.9 | 1.2 | 3.3 | 3.0 | 1.9 | 1.5 | 0.5 | 0.8 | — | 2.0 | 2.7 | 2.0 | — | — | — | — | — | 0.5 | 0.6 | 0.3 | 1.0 | — | 0.2 | 0.9 | 0.5 | 0.4 | — | 0.3 | 0.2 | 0.2 | 1.3 | 1.7 | 1.9 | 2.4 | 2.1 | 1.5 | 2.7 | 3.0 | 2.1 | 0.7 |
| 2017 | 3.1 | 2.2 | 1.4 | 1.2 | 2.2 | 2.7 | 3.1 | 1.3 | 1.3 | 1.2 | 2.7 | 0.9 | 1.9 | 1.3 | 0.5 | 0.9 | 1.2 | 0.6 | 0.5 | 0.8 | 1.0 | 3.4 | 2.9 | 1.8 | 1.3 | 0.5 | 0.7 | 3.1 | 2.1 | 2.3 | 2.1 | — | — | — | — | — | 0.5 | 0.6 | 0.3 | 1.1 | — | 0.2 | 0.9 | 0.4 | 0.4 | — | 0.3 | 0.2 | 0.2 | 1.2 | 1.6 | 1.9 | 2.3 | 2.0 | 1.4 | 2.4 | 2.9 | 2.1 | 0.7 |
| 2016 | 2.9 | 2.2 | 1.4 | 1.2 | 2.2 | 2.5 | 3.1 | 1.2 | 1.3 | 1.0 | 2.7 | 0.8 | 2.0 | 1.2 | 0.4 | 0.8 | 1.3 | 0.6 | 0.5 | 0.8 | 1.0 | 3.3 | 3.1 | 1.7 | 1.2 | 0.5 | 0.8 | — | 2.0 | 2.3 | 2.1 | — | — | — | — | — | 0.5 | 0.5 | 0.3 | 1.1 | — | 0.2 | 0.8 | 0.3 | 0.4 | — | 0.3 | 0.2 | 0.2 | 1.1 | 1.6 | 1.8 | 2.3 | 2.0 | 1.3 | 2.4 | 2.8 | 2.1 | 0.7 |
| 2015 | 2.9 | 2.2 | 1.3 | 1.2 | 2.2 | 2.4 | 3.1 | 1.2 | 1.2 | 1.0 | 2.9 | 1.2 | 2.2 | 1.5 | 0.6 | 1.0 | 1.3 | 0.7 | 0.5 | 0.8 | 1.0 | 3.2 | 3.1 | 1.9 | 1.3 | 0.5 | 1.0 | 3.1 | 1.9 | 2.3 | 2.2 | — | — | — | — | — | 0.6 | 0.5 | 0.3 | 1.1 | — | 0.2 | 0.8 | 0.4 | 0.4 | — | 0.3 | 0.3 | 0.2 | 1.0 | 1.6 | 1.8 | 2.3 | 2.0 | 1.4 | 2.3 | 2.8 | 2.1 | 0.7 |
| 2014 | 2.9 | 2.3 | 1.3 | 1.2 | 2.2 | 2.4 | 3.1 | 1.5 | 1.3 | 0.8 | 3.2 | 0.9 | 2.4 | 1.4 | 0.7 | 1.0 | 1.2 | 0.7 | 0.5 | 0.8 | 1.0 | 3.1 | 2.9 | 2.0 | 1.3 | 0.4 | 0.8 | — | 1.7 | 2.3 | 1.9 | — | — | — | — | — | 0.7 | 0.5 | 0.3 | 1.1 | — | 0.3 | 0.7 | 0.4 | 0.5 | — | 0.2 | 0.2 | 0.2 | 0.9 | 1.6 | 1.8 | 2.2 | 2.0 | 1.3 | 2.3 | 2.7 | 2.0 | 0.7 |
| 2013 | 2.8 | 2.2 | 1.3 | 1.3 | 2.2 | 2.3 | 3.0 | 1.6 | 1.3 | 0.8 | 3.3 | 0.8 | 2.6 | 1.7 | 0.6 | 1.0 | 1.2 | 0.7 | 0.5 | 0.8 | 0.9 | 3.3 | 3.0 | 1.9 | 1.4 | 0.4 | 0.6 | — | 1.6 | 1.6 | 1.7 | — | — | — | — | — | 0.7 | 0.7 | 0.3 | 1.0 | — | 0.3 | 0.7 | 0.4 | 0.4 | — | 0.1 | 0.2 | 0.2 | 0.8 | 1.5 | 1.8 | 2.2 | 2.0 | 1.3 | 1.8 | 2.7 | 2.0 | 0.7 |
| 2012 | 2.9 | 2.2 | 1.3 | 1.3 | 1.9 | 2.3 | 2.9 | 1.6 | 1.4 | 0.7 | 3.4 | 0.8 | 2.6 | 2.1 | 0.7 | 0.9 | 1.2 | 0.8 | 0.4 | 0.7 | 0.9 | 3.2 | 3.0 | 1.8 | 1.3 | 0.5 | 0.6 | 2.9 | 1.6 | 1.6 | — | — | — | — | — | — | 0.7 | 0.7 | 0.4 | 1.0 | — | 0.3 | 0.9 | — | 0.3 | — | — | 0.2 | 0.2 | 0.8 | 1.5 | 1.8 | 2.2 | 2.0 | 1.3 | 1.7 | 2.7 | 1.9 | 0.7 |
| 2011 | 2.8 | 2.2 | 1.2 | 1.3 | 1.9 | 2.2 | 2.7 | 1.6 | 1.5 | 0.7 | 3.6 | 0.7 | 2.4 | 2.3 | 0.7 | 0.9 | 1.4 | 0.7 | 0.5 | 0.7 | 0.8 | 3.2 | 2.9 | 1.5 | 1.2 | 0.5 | 0.5 | — | 1.6 | 1.7 | 2.4 | — | — | — | — | — | 0.7 | 0.7 | 0.3 | 1.0 | — | — | 0.7 | 0.3 | 0.2 | — | — | 0.3 | 0.2 | 0.8 | 1.5 | 1.7 | 2.1 | 1.9 | 1.2 | 1.8 | 2.7 | 1.8 | 0.8 |
| 2010 | 2.7 | 2.2 | 1.2 | 1.4 | 1.7 | 2.1 | 2.7 | 1.6 | 1.5 | 0.6 | 3.7 | 0.6 | 2.1 | 1.6 | 0.6 | 0.8 | 1.4 | 0.6 | 0.4 | 0.7 | 0.7 | 3.2 | 2.9 | 1.3 | 1.1 | 0.5 | 0.6 | — | 1.6 | 1.6 | — | — | — | — | — | — | 0.8 | 0.7 | 0.4 | 1.1 | — | — | 0.7 | — | 0.2 | — | — | 0.2 | 0.2 | 0.8 | 1.5 | 1.7 | 2.1 | 1.9 | 1.1 | 1.8 | 2.7 | 1.7 | 0.8 |
| 2009 | 2.7 | 2.2 | 1.2 | 1.4 | 1.7 | 2.0 | 2.6 | 1.6 | 1.6 | 0.6 | 3.7 | 0.5 | 1.8 | 1.4 | 0.5 | 0.8 | 1.6 | 0.5 | 0.4 | 0.8 | 0.7 | 3.4 | 3.1 | 1.3 | 1.1 | 0.4 | 0.5 | — | 1.7 | 1.7 | 2.6 | — | — | — | — | — | 0.8 | 0.6 | 0.5 | 1.3 | — | 0.0 | 0.8 | — | 0.2 | — | — | 0.3 | 0.3 | 0.8 | 1.5 | 1.7 | 2.1 | 1.9 | 1.1 | 1.8 | 2.8 | 1.7 | 0.8 |
* = Incomplete data (<70% population coverage) - some countries using forward-filled values from previous years
Showing latest 15 years. Total data spans 24 years
(2000 – 2023)
Visual Comparison
🇪🇺 Europe
1.77
100.0%
Key Insights
Best Performer
EU
HOW EUROPE
IS DEFINED
Transparent, population-weighted calculations for fair comparisons
01
Europe
Includes all 50 European countries:
- — 27 EU member states: Germany, France, Italy, etc.
- — Other European: UK, Switzerland, Norway, etc.
02
Simple Sum
Used for absolute totals across all countries:
- — Step 1: Collect metric for each country
- — Step 2: Add all values together
- — Examples: Total population, total GDP
03
Population-Weighted
Used for per-capita metrics to reflect population size:
- — Step 1: Metric × population per country
- — Step 2: Sum all weighted values
- — Step 3: Divide by total population
Example: GDP/Capita PPP
$48,942
2024
Source
Our World in Data