1. Sectoral Employment & AI Exposure Shares
Plot 1: Sectoral Employment & AI Exposure Shares
This chart displays the distribution of national employment across three sectors. The lighter background bars represent the total share of the respective sector in total employment. The solid foreground bars show the share of employees with high AI exposure (above the median). Hover over the individual bars to see the exact percentages.
Calculations:
Performed by Andy BAUSCH on Feb 2026 based on Eurostat (2025a, 2025b) and AIOE from Felten et al. (2023).
2. Total AI Exposure Based on High-Risk Occupations and Sectoral GVA Share
Plot 2: Total AI Exposure Based on High-Risk Occupations and Sectoral GVA Share
This chart illustrates the weighted AI exposure. It represents the proportion of highly exposed employees multiplied by their economic contribution to the Gross Value Added (GVA).
The Total Exposure Index (Et) is calculated as:
E_t = Σ (Sectoral Exposure * Sectoral GVA Share)
The colored segments represent the individual contribution of each sector to the national total, allowing for a comparison of which economic pillars drive a country's overall AI exposure. The sorting of the countries is dynamically determined by the sector placed first in the filter above.
Calculations:
Performed by Andy BAUSCH on Feb 2026 based on Eurostat (2025a, 2025b) and AIOE from Felten et al. (2023).
Methodology & References
Sectors:
This clustering distinguishes sectors based on their technology intensity and international tradability:
-
AI-intensive:
Includes Information & Communication (J), Financial and Insurance Activities (K), and Professional, Scientific and Technical Activities (M). These sectors show the highest affinity for cognitive tasks.
-
Tradable:
Agriculture (A), Mining (B), Manufacturing (C), Transportation (H), and Accommodation and Food Service (I). These industries either produce physical goods or are heavily integrated into international value chains.
-
Non-tradable:
Domestically oriented services such as Public Administration, Construction, Education, and Health.
Sources:
- Eurostat. (2025a). Employed persons by occupation and economic activity (NACE Rev. 2) (2008-2026): lfsa_eisn2 [Data set]. https://ec.europa.eu/eurostat/databrowser/view/lfsa_eisn2__custom_19029204/default/table* [Accessed: Dec 15, 2025].
- Eurostat. (2025b). Gross value added and income by detailed industry (NACE Rev. 2): nama_10_a64 [Data set]. https://ec.europa.eu/eurostat/databrowser/view/nama_10_a64__custom_19046323/default/table* [Accessed: Dec 20, 2025].
- Felten, E., Raj, M. & Seamans, R. (2023). How will Language Modelers like ChatGPT Affect Occupations and Industries? arXiv. Advance online publication. https://doi.org/10.2139/ssrn.4375268