Municipality variation
Municipalities with the largest differences between their schools
2 visualisationsSweden's most citable school data
Open, machine-readable data with full traceability. All sources documented, all measures defined, everything under CC BY 4.0.
65+ Visualisations
11 Data stories
19 Datasets
Quick start
Three steps from question to publication.
Theme packages
Curated collections of visualisations and stories, grouped by topic. Each package gives you ready-made material for reporting.
Teacher qualifications — where is the gap?
Municipalities with the lowest share of qualified teachers
2 visualisationsSALSA — better or worse than expected
Schools that consistently deviate from expected results
2 visualisationsIndependent school corporations
The largest corporations' schools, results and finances
2 visualisationsInspections and sanctions
Schools with ongoing injunctions and their history
1 visualisationSegregation and demographics
Areas with the greatest contrast between schools and neighbourhoods
2 visualisationsMethodology for theme packages
Key measures used in the theme packages above.
Merit score year 9
- Definition
- Average merit score for year 9 students. Calculated as the sum of the 17 best grades (max 340 points), where each grade gives 0–20 points.
- Unit
- poäng (0–340)
- Source
- Skolverket
- Level
- School level
Certified teachers
- Definition
- Share of teachers with pedagogical higher education (teaching degree or teacher certification) among all serving teachers.
- Unit
- % (0–100)
- Source
- Skolverket
- Level
- School level
SALSA score
- Definition
- Difference between actual and expected merit score given student composition. A positive value indicates the school performs better than expected.
- Unit
- poäng
- Source
- Beräknad (Skolverkets modell)
- Level
- School level
Cost per student
- Definition
- Total municipal cost per student per year, including teaching, premises, meals, student health, and administration.
- Unit
- kr/år
- Source
- Skolverket Statistikdatabasen
- Level
- School level
Tools
CSV export with metadata Download structured data with variable names, source and sync timestamp.
Shareable permalinks Every visualisation has a unique URL you can share or embed.
Citation guide Ready-made citation template for academic and journalistic use.
Metric cards Definition, source, quality level and limitations for every measure.
Analysis guide: How to use school data in reporting
From question to publication — a practical guide for journalists and analysts.
5 steps to your first data article
- Search for a municipality or school on the home page to get an overview of key metrics.
- Pick a theme package above — e.g. SALSA or teacher qualifications — for a ready-made angle.
- Compare municipalities or schools in the statistics views. Use filters to isolate school type and operator.
- Export CSV data with metadata and source attribution for your own analysis.
- Cite using our citation template below — proper attribution is required by CC BY 4.0.
Common pitfalls
Five common mistakes — and how to avoid them.
| Pitfall | Warning |
|---|---|
| Comparing raw merit values without SALSA | Raw merit values reflect pupil composition more than school quality. Use SALSA-adjusted values for fair comparisons. |
| Ignoring pupil count | Small schools have large statistical variation. Always check pupil count — a school with 15 pupils can swing 30 points between years. |
| Mixing school types | Compulsory and upper secondary schools are measured with different metrics. Make sure you compare within the same school type. |
| One year as a trend | At least 3 years of data are needed to identify trends. Use historical trends to see changes over time. |
| Using raw certification figures | Regional labour markets affect certification rates. Compare similar municipalities rather than absolute figures. |
Examples: how the data has been used
Three questions — and how we answered them with data.
How do independent schools compare with municipal ones?
Compares merit values, teacher certification and pupil counts between independent and municipal schools. The differences are smaller than the debate suggests.
Read the analysis → Which school groups deliver the best results?
Ranked overview of the largest independent school groups — pupil count, merit value and SALSA deviation side by side.
Read the analysis → Where is the teacher shortage felt the most?
Maps teacher certification by county and subject. Shows that certain subjects face a chronic shortage nationwide.
Read the data story →All 11 data stories · All data-driven analyses
Citation and licence
All data on Skolkoll is available under CC BY 4.0. You may freely use, share and adapt the material — as long as you credit the source.
Citation template
Skolkoll (2026). [Title of visualisation/dataset]. Skolkoll.se. Retrieved [date] from [URL].
BibTeX
@misc{skolkoll2026,
author = {Skolkoll},
title = {[Title of visualisation/dataset]},
year = {2026},
url = {[URL]},
note = {Retrieved [date]}
}
Press enquiries: info@skolkoll.se
Data updates
When is the data updated and how do you know if something has changed?
| Source | Frequency | Content |
|---|---|---|
| Skolverket | Daily | School units, key metrics, SALSA |
| Kolada | Monthly | Municipal statistics, demographic measures |
| SCB | Monthly | Education level, income data |
| Schools Inspectorate | Quarterly | Inspection decisions, injunctions |
| Companies Registration Office | Weekly | Operators, corporate structure |
How do you know if data has changed? Every CSV export includes a sync timestamp. The download page shows the latest update per dataset.
About the data
Update frequency Daily sync from sources. Skolverket API, Kolada, SCB and the Schools Inspectorate.
Data sources Skolverket, Kolada (municipal statistics), SCB, Companies Registration Office and the Schools Inspectorate.
Method Standardised definitions, SALSA model for benchmarking, documented limitations.
Traceability Every measure has a source reference, version information and change log.
All data available under CC BY 4.0