Occupation Classification
ISCO-08
International Standard Classification of Occupations (2008 version). A global system that groups all jobs into standardised categories so data from different countries can be compared fairly. WorldJobsData uses it as the common language for every country's workforce.
Example: "Managers" is ISCO group 1. "Professionals" is group 2. These groups exist in every country's data.
ISCO Major Group
The broadest level of job classification - 9 groups numbered 1-9 (plus group 0 for Armed Forces). Every country in WorldJobsData shows at least this level. Each rectangle in the treemap is one major group.
Groups: 1 Managers - 2 Professionals - 3 Technicians - 4 Clerical - 5 Service & Sales - 6 Agriculture - 7 Craft & Trades - 8 Machine Operators - 9 Elementary
Employment Share (%)
What percentage of a country's total working population is in each occupation group. The size of each rectangle in the treemap reflects this - bigger rectangle = more workers in that group.
BLS Occupation (US only)
For the United States, WorldJobsData has data for 341 individual job titles from the US Bureau of Labor Statistics Occupational Outlook Handbook. These are much more specific than ISCO groups. Only the US has this level of detail.
Data Tiers
Tier A - Full data
21 countries with occupation-level wage data from official national statistics. Employment, AI scores, gender breakdown, and actual salary figures are all shown.
Countries: US, UK, Germany, Australia, Canada, Netherlands, Spain, Poland, and 13 more EU/EEA countries.
Tier B - Employment only
19 countries where we have occupation employment data from Eurostat but wages were not available or suppressed. You will see the treemap, AI scores, and gender data - but no pay figures.
Countries: France, Italy, Sweden, Denmark, Finland, and others.
Tier C - ILO data
166 countries with workforce data sourced from the ILO (International Labour Organization). Employment by occupation group, gender breakdown, informal employment rate, and time-series trend are available. No wage data.
Most countries in Africa, Asia, Latin America, and the Middle East fall here.
Color Layers
AI Exposure Score
A score from 0 to 10 estimating how much generative AI will reshape the tasks in this occupation. Higher score = work will change significantly. Does not directly predict job loss.
Score 8-10: Heavy AI integration expected (e.g. Professionals, Clerical). Score 1-3: Minimal AI impact (e.g. Elementary labour, Skilled trades).
Robotics Risk
A score from 0 to 10 for physical automation exposure - robots and machines replacing manual tasks. Separate from AI: a factory job may score low on AI but high on robotics.
Offshoring Risk
How easily could this work be moved to a different country? High scores indicate tasks that can be done remotely across borders. Low scores indicate work that must be local.
WFH Potential
Work From Home potential - how much of this occupation can be performed remotely. Based on task characteristics (digital vs physical, client-facing vs back-office).
Pay Layer
Annual wage in USD for each occupation group, from official government statistics. Tier A countries only. Color goes from darker (lower pay) to brighter (higher pay). Wages are median unless labeled "mean".
Growth Layer
Projected 10-year employment growth (%) for each occupation. US only - only the BLS publishes this internationally comparable dataset. All other countries show "no data" on this layer.
Gender Layer
The female share (%) of workers in each occupation group. Darker = fewer women. Brighter = more women. Available for 204 countries via ILO data.
Wages & Pay
Median Wage
The middle salary - half of workers earn more, half earn less. Less distorted by very high earners than an average. Most Tier A countries report median wages.
Mean Wage
The average salary across all workers in the group. Can be pulled higher by a small number of very high earners. Australia and several EU countries only publish mean wages. The UI flags this with a "mean" label.
OECD Wage Benchmark
The national average annual wage in USD (adjusted for purchasing power parity) published by the OECD for 38 countries. A country-wide number, not occupation-specific. Shown in insight cards as a reference point.
Workforce & Economy Terms
Informal Employment
Work that happens outside formal employment arrangements - no written contract, no social protection, not registered with tax authorities. Common in developing economies. A high informality rate means the AI-exposure scores should be read with extra caution.
India: ~87% informal. Nigeria: ~93% informal. Germany: ~3%.
Female Labor Force Participation
The share of women in the total working-age population who are employed or actively looking for work. Shown in the Gender insight card as a country-level figure.
Workforce-Weighted Average
When computing a single headline score for a country, we weight each occupation by how many people work in it. A large occupation like Professionals has more influence on the headline than a small one like Armed Forces.
15-Year Workforce Shift
The change in employment share for each ISCO group between ~2010 and ~2025. Reveals whether a country is shifting toward more knowledge work, more service work, or shedding certain sectors over time.
Getting started
1
Pick a country
Use the dropdown at the top of the page. 206 countries are available. The treemap loads immediately and shows how the workforce is distributed across occupation groups.
2
Choose a color layer
Click one of the 7 buttons: AI - Robotics - Offshore - WFH - Pay - Growth - Gender. The treemap recolors instantly. The legend at the bottom left updates to show what the colors mean.
3
Hover or click any block
Hovering shows a tooltip with the occupation name, employment share, wage (if available), and all four risk scores. Clicking a block in Scatter view highlights it.
4
Read the insight cards below the chart
Up to 15 cards appear below the visualization for each country. They summarize key findings: workforce composition, AI risk profile, gender breakdown, wage benchmarks, trend over 15 years, and more.
Views
Treemap
Default view. Each rectangle = one occupation group. Rectangle area = share of total employment. Color = selected layer value. Best for seeing the overall shape of a country's workforce at a glance.
Detail (US only)
Shows all 341 individual US occupations from BLS data in a full-screen treemap. Much more granular - you can see "Software Developers" separately from "Accountants". Only available for the United States.
Scatter
AI exposure on the X-axis, median pay on the Y-axis. Each bubble = one occupation group. Bubble size = employment. Instantly shows which groups are both high-paid AND high-AI-exposed.
Compare
Click "Compare" to open a two-panel view. Choose two countries and compare their treemaps side by side under the same color layer. Useful for seeing structural differences between economies.
Insight cards
Headline
The workforce-weighted average AI/Robotics/Offshore/WFH score for the whole country. One number that summarises overall exposure on the selected layer.
Risk Profile
Four horizontal bars showing all four dimension scores side by side. Lets you see whether a country's risk is concentrated in AI, Robotics, Offshoring, or WFH.
AI Exposure Mix
A stacked bar breaking down what share of the workforce falls into low / medium / high AI exposure bands.
World Comparison
Where this country ranks among all 206 countries on the selected layer. Shows the top 3 and bottom 3 countries as reference points.
Highest / Lowest Exposure
The top 5 occupation groups with the highest and lowest score on the current layer within this country.
Largest Occupations
The biggest groups by employment share - where most workers actually are.
15-Year Trend
How each ISCO group's share of total employment has changed from ~2010 to ~2025. Growing groups shown in green, shrinking in red.
Gender Mix
Female share per occupation group, plus country-level female labor force participation rate.
Informal Employment
The share of the workforce in informal work. Includes a caveat: high informality means AI-exposure scores should be read with extra caution.
Pay Range
The ratio of the highest-paid to lowest-paid occupation group - a simple measure of wage inequality within the country's workforce.
OECD Benchmark
The country's national average annual wage vs the OECD average. Available for 38 countries.