ISCO-08 Global standard job classification system. Groups all occupations into 9 major categories so every country uses the same labels.

Tier A 21 countries with full data: employment + official wages + AI scores + gender. Richest insight cards.

Tier B 19 European countries with employment + AI scores + gender - but no wage data (suppressed or unavailable).

Tier C 166 countries with ILO data: employment, gender, informal rate, time-series. No wages.

AI Exposure (0–10) How much generative AI will reshape tasks in this occupation. Higher = bigger change expected. Not a job-loss prediction.

Robotics Risk (0–10) Physical automation exposure - robots and machines replacing manual tasks. Separate from AI.

Offshoring Risk (0–10) How easily this work could move to another country. High = digital/remote-compatible tasks.

WFH Potential (0–10) How much of this occupation can be done remotely.

Employment Share What % of the country's workforce is in this occupation group. Controls rectangle size in the treemap.

Informal Employment Work done without a formal contract or social protection. Common in developing economies. A high rate means AI-exposure scores should be read with caution.

Median vs Mean Wage Median = the middle salary (more accurate). Mean = the average (skewed by top earners). AU and some EU countries only publish mean wages - labeled in the UI.

OECD Benchmark Country-wide average annual wage (USD PPP) for 38 countries - used as a reference point, not a layer.

Detail view US only - 341 individual BLS job titles instead of 9 ISCO groups. Click "Detail (341)" when US is selected.

Treemap Rectangle area = employment share. Color = selected layer. Default view.

Scatter AI exposure (X) vs median pay (Y). Bubble size = employment. Shows high-pay + high-AI groups.

Compare Two countries side by side under the same layer. Click "Compare" in the header.