Data Verification
We automatically check whether our data matches the official sources (Eurostat, Statistik Austria). This way you can be sure the numbers shown are accurate.
Simple verification tests confirming the displayed data is consistent and plausible.
Total jobs match official EU data
We add up all jobs in our tool and compare with the official Eurostat number for Austria (4,731,970 employed persons in 2024).
Every occupation has workers
No occupation in our list should show 0 workers — that would mean missing data.
Every occupation has salary data
All occupations must have salary info from Statistik Austria's earnings survey (VSE 2022).
Salaries are realistic for Austria
All annual gross salaries should fall between €15,000 and €150,000 — the typical range for Austrian occupations.
Education levels are correctly assigned
Every occupation must have a recognized education level (e.g. apprenticeship, bachelor, master).
AI exposure scores are valid (0–10)
Each occupation's AI exposure score (how much AI could affect the job) must be between 0 (no impact) and 10 (maximum impact).
Job outlook scores are valid (-10 to +10)
Each job outlook score (will there be more or fewer jobs in the future?) must be between -10 (strong decline) and +10 (strong growth).
No duplicate occupations
Each occupation appears exactly once — no duplicates that could distort the data.
Every occupation cites its data source
All data must be traceable — every occupation references the official source it was calculated from (Eurostat, Statistik Austria).
Manufacturing jobs match Eurostat
Our manufacturing sector total (NACE C) should exceed 600,000 — Eurostat reports ~690,000 for Austria.
IT sector jobs match Eurostat
Our IT sector total (NACE J) should exceed 140,000 — Eurostat reports ~155,000 for Austria.
Enough occupations covered
Our tool should include at least 50 different occupations for a meaningful picture of Austria's job market.
Salary inequality (Gini) within expected range
The Gini coefficient of salaries across occupations should be 0.10–0.35 — consistent with Austria's compressed wage structure (OECD: ~0.27 for gross earnings).
Salary variation coefficient is plausible
The coefficient of variation (CV = std dev / mean) of salaries should be 0.15–0.50 — too low means no differentiation, too high means outliers dominate.
AI exposure correlates positively with pay
Higher-paid knowledge work tends to have higher AI exposure (Eloundou et al. 2023). Pearson r should be > 0 (positive correlation).
Higher education → higher median salary
Occupations requiring a Master's/PhD should have a higher median salary than those requiring only compulsory school — a core labor economics prediction (Mincer 1974).
Services employ more than industry (tertiarization)
Austria's economy is ~70% services (WIFO). Service sectors (G–S) should employ more than goods-producing sectors (A–F).
Health sector is a major employer
Austria's aging population drives health employment. NACE Q (Health & Social) should exceed 400,000 (Eurostat: ~440K).
Construction sector within Eurostat range
Austria's construction sector (NACE F) should have 250,000–350,000 workers (Eurostat 2024: ~300K).
All 19 NACE sections represented
The dataset must cover all economic sectors A through S — missing a sector means an incomplete picture of Austria's economy.
Weighted median pay matches national average
The employment-weighted average salary should be near Austria's national median (~€35,000–€45,000 gross/year, Statistik Austria 2022).
AI exposure uses full score range
AI exposure scores should span at least 7 points (e.g. 1–8) to show meaningful differentiation between physical and cognitive jobs.
Physical occupations have low AI exposure
Construction & agriculture workers (NACE A, F) should average < 4 on AI exposure — these jobs resist digital automation (Autor 2015).
Knowledge-intensive sectors have high AI exposure
IT, finance, and professional services (NACE J, K, M) should average > 5 on AI exposure — these are cognitive-task-heavy sectors (Felten et al. 2021).
Employment is not overly concentrated (HHI)
The Herfindahl-Hirschman Index of employment across NACE sections should be < 0.15 — a diversified economy like Austria should not be dominated by a single sector.
Education levels follow expected distribution
Most Austrian occupations require mid-level qualifications (apprenticeship–bachelor). The share of extreme levels (compulsory only or PhD) should be < 30%.
Total annual wage bill is economically plausible
The sum of (salary × jobs) across all occupations should approximate Austria's compensation of employees (~€190–230 billion, Eurostat nama_10_gdp).
Wholesale & retail trade is largest private employer
NACE G (Trade) has historically been Austria's largest private-sector employer at ~550K–650K (WKO). Our data should reflect this.
All AI exposure scores have written rationales
Every occupation must include a text rationale explaining why its AI exposure score was assigned — required for scientific reproducibility.
Job outlook is not systematically biased
The employment-weighted mean outlook should be near 0 (±3) — extreme positive or negative bias would indicate systematic over-optimism or pessimism.