Employment statistics on high-tech industries and Knowledge Intensive Services at the national level (1995-2007, NACE Rev. 1.1) (Eurostat)
Employment statistics on high-tech industries and Knowledge Intensive Services at the national level (1995-2007, NACE Rev. 1.1): data for 31 countries and regions. Source: Eurostat. Compare countries, chart the series, or query it free as CSV, JSON, or API at https://autario.com/data/employment-statistics-on-high-tech-industries-and-knowledge-intensive-services-at-the-national-level-1995-2007-nace-rev-1-1-eurostat.
9,321 rows · 15 columns · 31 regions/countries · Last data pull: 2026-11-03
This dataset provides comprehensive employment statistics for high-tech industries and knowledge-intensive services across European nations during a transformative period for the continent's economy. Compiled by Eurostat using the NACE Rev. 1.1 classification system, it captures workforce dynamics in sectors that became increasingly central to economic growth and competitiveness during the late 1990s and 2000s.
The dataset encompasses 31 entities with 9,321 total observations spanning from 1995 to 2007. Most major European countries including the UK, Austria, Belgium, Germany, France, and the Nordic nations reported data through 2007, providing a consistent 12-year window for trend analysis. A handful of nations have earlier cutoff dates, with Switzerland's latest data from 2001 and Malta's from 2002, reflecting varying statistical reporting schedules across Europe at that time.
Employment in high-tech and knowledge-intensive sectors showed consistent expansion during this period, with major economies maintaining steady growth trajectories. The data captures a critical era when European policymakers began prioritizing the transition from manufacturing toward innovation-driven industries. Analysts can track how different member states invested in developing their knowledge economies and how employment patterns diverged across the continent during pre-financial crisis expansion.
Researchers, policy analysts, and economists use this dataset to understand historical employment patterns in strategic sectors, benchmark national performance, and identify which countries successfully transitioned toward knowledge-based work. The granular coverage by country makes it invaluable for comparative European studies and for tracking long-term shifts in industrial composition.
Use this data
Take Employment statistics on high-tech industries and Knowledge Intensive Services at the national level (1995-2007, NACE Rev. 1.1) (Eurostat) beyond this page: ask an AI assistant about it, chart it in a data app, or set it next to the numbers from your own accounts.
Query via API instead of re-downloading. Live REST API and instant in-browser charts stay source-synced with the primary publisher, no CSV exports to maintain, MCP-ready for Claude, ChatGPT, and Cursor. Use this dataset in Power BI or Excel via the public API, no key required.
Indicator: eu_employment_statistics_on_high_tech_industries_and_knowledge__d35daa
Show API + MCP usage examples
# Get data (JSON)
curl "https://autario.com/api/v1/public/datasets/d35daa4f-7adf-47a5-9d14-7975a10ee332/data?limit=100"
# Filter and sort on this dataset's own columns (replace the bracketed placeholder with a value)
curl "https://autario.com/api/v1/public/datasets/d35daa4f-7adf-47a5-9d14-7975a10ee332/data?filter=geo:eq:<geo>"
# CSV export (login required)
curl "https://autario.com/api/v1/public/datasets/d35daa4f-7adf-47a5-9d14-7975a10ee332/export"
# Python
import requests
r = requests.get("https://autario.com/api/v1/public/datasets/d35daa4f-7adf-47a5-9d14-7975a10ee332/data", params={"limit": 100})
data = r.json()["data"]
Full API docs · Agent guide · MCP setup for Claude / ChatGPT / Cursor · No API key required for public data.
For AI agents
Statistics for this dataset are precomputed server-side. One call returns finished, verified numbers with provenance. No row download, no local computation:
# Finished stats, zero rows (n, min, max, avg, first/last)
curl "https://autario.com/api/v1/public/datasets/d35daa4f-7adf-47a5-9d14-7975a10ee332/data?summary_only=true"
# Summary statistics (mean/median/std/quartiles/skew)
curl "https://autario.com/api/v1/public/ontology/stats/describe?indicator=eu_employment_statistics_on_high_tech_industries_and_knowledge__d35daa&entity=AL"
# Growth: latest period-over-period change
curl "https://autario.com/api/v1/public/ontology/stats/pct-change?indicator=eu_employment_statistics_on_high_tech_industries_and_knowledge__d35daa&entity=AL"
# Smoothed trend: 5-period rolling mean
curl "https://autario.com/api/v1/public/ontology/stats/rolling?indicator=eu_employment_statistics_on_high_tech_industries_and_knowledge__d35daa&entity=AL&window=5"
# Correlate with any other indicator (ids via /api/v1/public/ontology/indicators)
curl "https://autario.com/api/v1/public/ontology/stats/correlate?entity=AL&a=eu_employment_statistics_on_high_tech_industries_and_knowledge__d35daa&b=<indicator_id>"
The same operations are MCP tools (describe, pct_change, rolling_stats, correlate) via npx autario-mcp or the remote endpoint https://autario.com/mcp. MCP responses default to TOON format and use about 74% fewer tokens than pretty JSON. Details: agent guide and llms.txt.
Original source: Eurostat
Sample rows
| geo | dataflow | last_update | freq | indic_sb | nace_r1 |
|---|
| AL | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
| AT | ESTAT:HTEC_EMP_SBS(1.0) | 18/02/10 11:00:00 | A | V13310 | D |
Related datasets
Keywords: EU, SDMX
Open Employment statistics on high-tech industries and Knowledge Intensive Services at the national level (1995-2007, NACE Rev. 1.1) (Eurostat) on autario.com