About This Dashboard
This dashboard brings together monthly employment statistics from Statistics Sweden (SCB) and AI-exposure scores from the DAIOE framework to support research into how AI may be reshaping labour market outcomes across Swedish occupations.
Data Sources
| Source | Description |
|---|---|
| Labour Force Survey (AKU), SCB | Monthly counts of employed persons (SCB's "total employment" category) and changes, by occupation and gender |
| DAIOE Framework | Data-driven AI Occupational Exposure scores across multiple AI capability sub-domains |
Coverage
- Geography: Sweden (national totals)
- Occupation level: SSYK 2012 major groups (1-digit classification, 9 categories)
- Time range: 2015-Jan to 2026-Aug, updated monthly
- Employment unit: thousands of people (e.g. 150 = 150,000)
- Missing values: where SCB suppresses an estimate for a small group in a month (five months for women in the agricultural, horticultural, forestry and fishery group), the value is left blank, not shown as zero
- Exposure gaps: DAIOE has no image comprehension scores for 2015, so that sub-domain is left out of the exposure chart for that year
Key Concepts
SSYK 2012 The Swedish Standard Classification of Occupations (2012 edition). Groups all occupations into 9 broad major categories based on skill level and field of work.
DAIOE: AI Exposure Scores Data-driven AI Occupational Exposure scores quantify how strongly the tasks within an occupation may be affected by different AI capabilities. Scores are computed across multiple sub-domains (e.g. language, vision, reasoning) and aggregated as weighted averages at the occupation level.
Percentile Rank Shows where an occupation sits relative to all others on a given sub-domain. A percentile rank of 80 means the occupation scores higher than 80% of all occupations; it is a relative, not absolute, measure.
Exposure Level An ordinal scale from 1 (Very Low) to 5 (Very High) summarising the weighted-average AI exposure score for a sub-domain. Used for quick comparisons; the underlying index score provides more precision.
Employment Change Month-to-month or multi-month percentage change computed from absolute employment counts. Positive values indicate growth; negative values indicate decline. Changes are computed from aggregated employment counts and absolute changes, not by averaging gender-specific percentage rates.
Caveats
- AI exposure measures potential task-level exposure to AI capabilities. It is not a prediction of employment decline, job loss, or automation outcomes.
- Month-to-month employment changes are volatile and may reflect seasonal patterns, survey revisions, or reclassifications unrelated to AI adoption.
- Percentile ranks are relative to other occupations in the dataset. A high rank does not imply a high absolute exposure score, and rankings may shift as new occupations or years are added.
- SSYK 2012 major groups are broad; occupations within a group can vary considerably in their actual AI exposure.
About the Project
This tool is developed by the AI-Econ Lab as part of ongoing research into the intersection of artificial intelligence and labour markets. For questions or collaboration enquiries, please visit ai-econlab.com.