Fastest growing workplaces

Analysis: why are some at the top and others at the bottom?

We compared change in jobs with every other municipal figure we have, from Statistics Norway, NIPH and the Brønnøysund Register Centre. Below are the figures that go most closely together with change in jobs across the country, and what marks the municipalities at the top and bottom on those figures.

The pattern across the country

• Municipalities that rank high on «Centrality (Statistics Norway's centrality index, 6 is most central)» most often also rank high on change in jobs (moderate association, rank correlation 0.45 across 355 municipalities; Statistics Norway, centrality index).
• Municipalities that rank high on «Population on 1 January» most often also rank high on change in jobs (moderate association, rank correlation 0.43 across 355 municipalities; Statistics Norway table 11342).
• Municipalities that rank high on «Gross operating revenue per inhabitant» most often rank low on change in jobs (moderate association, rank correlation −0.42 across 355 municipalities; Statistics Norway table 12137).
• Municipalities that rank high on «Gross income per person aged 17 and over, mean» most often also rank high on change in jobs (moderate association, rank correlation 0.42 across 355 municipalities; Statistics Norway table 03068).

The highest and the lowest

• No. 1 Hamarøy (48.5%) The municipality does not stand out on the figures that otherwise go together with the measure. The explanation probably lies in something we have no figures for. Goes against the pattern: «Gross income per person aged 17 and over, mean» 534,700 kr (no. 292 of 357 from the top; Statistics Norway table 03068). Most distinctive industry: produksjon av andre ikke-metallholdige mineralprodukter, 14% of the jobs, 43.0 times the national share (Statistics Norway table 13470, 2025).
• No. 2 Froland (27.3%) Fits the pattern: «Centrality (Statistics Norway's centrality index, 6 is most central)» 3 (no. 81 of 357 from the top; Statistics Norway, centrality index). Most distinctive industry: spesialisert bygge- og anleggsvirksomhet, 10% of the jobs, 2.2 times the national share (Statistics Norway table 13470, 2025).
• No. 3 Gjerstad (22.1%) The municipality does not stand out on the figures that otherwise go together with the measure. The explanation probably lies in something we have no figures for. Goes against the pattern: «Gross income per person aged 17 and over, mean» 506,900 kr (no. 339 of 357 from the top; Statistics Norway table 03068). Most distinctive industry: arbeidskrafttjenester, 16% of the jobs, 13.9 times the national share (Statistics Norway table 13470, 2025).
• No. 355 Hyllestad (−17.6%, among the lowest) Fits the pattern: «Population on 1 January» 1,275 (no. 315 of 357 from the top; Statistics Norway table 11342); «Gross income per person aged 17 and over, mean» 530,000 kr (no. 305 of 357 from the top; Statistics Norway table 03068); «Gross operating revenue per inhabitant» 184,722 kr (no. 66 of 357 from the top; Statistics Norway table 12137). Most distinctive industry: produksjon av andre transportmidler, 21% of the jobs, 39.0 times the national share (Statistics Norway table 13470, 2025).
• No. 354 Etnedal (−16.0%, among the lowest) Fits the pattern: «Gross income per person aged 17 and over, mean» 460,300 kr (no. 357 of 357 from the top; Statistics Norway table 03068); «Population on 1 January» 1,593 (no. 302 of 357 from the top; Statistics Norway table 11342). Most distinctive industry: jordbruk og tjenester tilknyttet jordbruk, jakt og viltstell, 9% of the jobs, 7.2 times the national share (Statistics Norway table 13470, 2025).
• No. 353 Midtre Gauldal (−16.0%, among the lowest) The municipality does not stand out on the figures that otherwise go together with the measure. The explanation probably lies in something we have no figures for. Most distinctive industry: produksjon av trelast og varer av tre, kork, strå og flettematerialer, unntatt møbler, 7% of the jobs, 18.6 times the national share (Statistics Norway table 13470, 2025).

The analysis describes what goes together, not what causes what. Two figures can follow each other because both depend on something else, such as size, location or industry. The figures are from the latest year available for each source, so the years can differ. Rank correlation runs from −1 to 1; values near 1 mean the municipalities lie in roughly the same order on both measures.