Why an AI labor market index?

Because artificial intelligence is advancing rapidly and could have major consequences for the labor market. The tools keep getting better, but it takes time before firms adopt them and reorganize how work is done. The effect may therefore arrive gradually: first in which jobs are advertised, who gets hired, or which age groups grow more slowly. The AI Labor Market Index tracks such changes continuously, month by month.

For the impatient reader

All figures on this page cover wage earners in the Norwegian private sector (the public sector is not included).

  • No job crisis in Norway: total private-sector employment is growing, and the employment rate is stable.
  • The AI Labor Market Index is slightly positive: since the period before ChatGPT, total employment in the most AI-exposed occupations has grown about as much as in the least exposed.
  • But there are important differences across age: among the youngest (21–30) in the most AI-exposed occupations, employment has fallen — both relative to young people in other occupations and relative to older workers in the same occupations. The decline has intensified over the past year.
  • Two mechanisms pull in opposite directions: in occupations where AI usage is most augmenting (AI assists the worker), employment has grown clearly more than in other occupations since the launch of ChatGPT, while growth has been clearly weaker where usage is most automating (AI performs the task).

The index tracks employment in the Norwegian private sector using microdata.no, provided by Statistics Norway (SSB). Occupations are grouped by how exposed each occupation is to large language models (Eloundou et al. 2024) and by workers' age — a Norwegian parallel to Stanford Digital Economy Lab (DEL)'s Canaries Dashboard, built on register data for the entire population of private-sector wage earners.

AI Labor Market Index
The AI Labor Market Index shows whether job growth since ChatGPT has been stronger or weaker in the occupations where AI can perform the most tasks, compared with the occupations where it can perform the fewest. A positive number means the most AI-exposed occupations have grown the most; a negative number means they have grown the least — an early warning that AI may be displacing jobs.
How the number is calculated Employment growth in the most AI-exposed occupations minus growth in the least exposed: the average of the last three months against the level in October 2022, the month just before ChatGPT. We use October 2022 as the reference to avoid the differential post-pandemic recovery in 2021–2022. The adjustment follows the selection below.

Index = 100 in November 2022 (launch of ChatGPT)

1 · Employment by AI exposure

The figure shows whether jobs where AI can do the most evolve differently from the rest of the labor market. The 397 occupations (STYRK-08) with an exposure score from Eloundou et al. (2024) are split into five equal-sized groups, from quintile 1 (least exposed to language models) to quintile 5 (most exposed). The lines show each group's outcome — employment, new hires or FTE-adjusted average pay, depending on the selection above — indexed to 100 in November 2022. If a line lies below the others, that group has had weaker development since ChatGPT arrived. The steep rise in 2021–2022 (shaded area) is the labor market's post-pandemic recovery; because it lifted all groups, October 2022 — not the whole period — is used as the reference for the AI Labor Market Index.

2 · Age × AI exposure

If AI particularly affects those with the least work experience, the effects should show up first among the young in the most exposed occupations. Choose an age group and see the development by exposure quintile.

3 · Employment by age

The same set of occupations, grouped by workers' age. If AI first displaces those with the least experience, it is the youngest groups that are the “canaries in the coal mine”.

4 · Employment shares in November 2022

Each group's share of total employment in the sample in November 2022. Unlike the U.S. ADP panel, where two-thirds of employment sits in the two most exposed quintiles, Norwegian private-sector employment is fairly evenly distributed.

Occupation cases

Four occupations often raised in the discussion of AI and work: software developers and customer service reps (high exposure), electricians and home health aides (low exposure). Index by age group within the occupation.

5 · Software developers

STYRK-08 2512–2514 and 2519. About 26,000 employees in the private sector (ages 21–60) in November 2022. High AI exposure.

6 · Customer service reps

STYRK-08 4222 (call-centre employees). About 4,000 employees in the base month; the series for the oldest groups are based on few people and are volatile. High AI exposure.

7 · Electricians

STYRK-08 7411. A skilled trade with low AI exposure.

8 · Home health aides

STYRK-08 5322. About 9,000 employees in the private sector in the base month; the large municipal part of the occupation is not included. Low AI exposure.

9 · Growth by AI usage

Occupations grouped by the share of the occupation's Claude requests that are automating (top) or augmenting (bottom), cf. figure 10 below. Employment, all ages combined; the adjustment follows the selection above. Source: Anthropic Economic Index (Handa et al. 2025).

10 · Employment by AI usage

The exposure measures are estimates of what language models can do. Here occupations are instead grouped by observed use of Claude (Anthropic Economic Index, Handa et al. 2025): the share of the occupation's requests that are automating (the model performs the task) or augmenting (the model assists). “No usage” is occupations below the query threshold.

11 · Shares by usage group

Each group's share of total employment at the normalization point, for the usage pattern selected above.

Last 12 months: Before and after agentic AI

The dots show the change over the past twelve months by group; the horizontal line is the spread between groups. The outcome selection above applies here too. Source: The AI Labor Market Index (kiindeksen.no), the A-ordningen via microdata.no, Eloundou et al. (2024).

Method

The data source is the A-ordningen (employers' monthly reporting) via microdata.no: the entire population of wage earners in the Norwegian private sector, monthly from January 2021, in the age groups 21–30, 31–40, 41–50 and 51–60. Occupations are linked to AI exposure from Eloundou et al. (2024) and to observed Claude usage from Anthropic Economic Index. By default the series are shown seasonally adjusted and smoothed with a 6-month moving average; raw series, other smoothing and population-adjusted (per capita) variants can be chosen above. Expand the items below for full details.

Data source and scope

The A-ordningen is employers' monthly reporting of all employment relationships to the authorities; we use it via microdata.no, provided by SSB. The sample is all private-sector wage earners, ages 21–60. The reference period for each month is the week containing the 16th. The “Canaries sample” is the 397 STYRK-08 occupation codes that have an exposure score; it covers the large majority of private-sector employment.

AI exposure and usage groups

Each occupation (four-digit STYRK-08) is linked to the exposure measure “beta” from Eloundou et al. (2024, Science) via the occupation classifications ISCO-08 and SOC. Beta measures the share of an occupation's tasks that can be performed substantially faster with large language models and associated software. The quintiles are equally weighted per occupation (not employment-weighted), the same construction as in the DEL dashboard. The usage groups in figures 9–11 are instead based on observed use: the share of the occupation's Claude requests classified as automating or augmenting in the Anthropic Economic Index (Handa et al. 2025), grouped as in Brynjolfsson, Chandar and Chen (2025).

Outcomes: employment, new hires and pay

Employment is the number of wage earners in the group. New hires is the number of jobs with a registered start date in the window between the previous and current monthly reference date; the series is strongly seasonal, so a moving average or seasonally adjusted view is recommended. Pay is the group's average monthly cash pay, scaled up to full-time equivalent (FTE) using each occupation-age cell's average contracted hours — so a part-time position counts as its share of a full-time year and pay changes are not conflated with changes in working time; the pay series are nominal. All series are indexed to 100 in the reference month (November 2022, or February 2025 when Claude Code is selected).

Seasonal adjustment

Norwegian labor market series have strong seasonal variation (among other things the January dip in the A-ordningen and the summer peak in new hires). The seasonal adjustment is a transparent X-11 core, computed separately for each series:

  1. The series is taken in logarithms, so the seasonal pattern is treated as multiplicative.
  2. The trend is estimated with a centered 2×12 moving average (13 months, half weight on the endpoints), which is seasonally free by construction.
  3. The seasonal factor for each calendar month is the average of the deviations from the trend for that month, normalized to sum to zero — the adjustment does not shift the level.
  4. The factors are estimated on January 2021–December 2024 and then frozen: new months do not change history (releases are stable vintages), and any AI effect after 2024 cannot leak into the seasonal factors and be adjusted away.

Built-in check: twelve-month changes compare the same calendar month and are seasonally free by construction; in the current release they are practically identical computed on raw and seasonally adjusted series (correlation 1.000).

Population adjustment (per capita)

The population grows differently across age groups, so pure headcount series mix labor-market changes with demographics: a group can have more employed simply because there are more people of that age. The per capita variants therefore divide the number employed by the resident population in the same age group (SSB table 07459, quarterly figures interpolated to month; for series covering all ages, the combined population aged 21–60 is used). The index then measures jobs per person — the employment rate — rather than the number of jobs. Example: young and old in combined headcount figures can look very different even when their employment rate develops the same way.

Smoothing

The smoothing is a centered moving average over 3 or 6 months, computed in the browser. At the endpoints the available part of the window is used, so the most recent month is always shown. Smoothing and seasonal adjustment can be combined freely; the default view is seasonally adjusted with a 6-month average.

Differences from the DEL dashboard

DEL uses a balanced panel of ~25,000 U.S. firms with ADP payroll data; we use the entire Norwegian population of private-sector wage earners without sample selection. Our age groups are decade groups (theirs are finer). The composition is very different: in the ADP panel 66 percent of employment sits in the two most exposed quintiles, in Norway about 40 percent. Aggregate cross-country comparisons must take this into account. DEL publishes only raw series; the seasonal and population adjustments are our additions, and our raw series reproduce their method exactly.

Revisability and releases

The A-ordningen can revise recent months. Each monthly data release is therefore archived as its own vintage and is not changed afterwards; the site always shows the latest release, and all earlier ones can be downloaded.

The AI Labor Market Index is produced by Øystein Hernæs (Frisch Centre) and Andreas R. Kostøl (BI Norwegian Business School). A research paper with a full methodological description and analysis is in progress; see citation information. Contact: andreas.r.kostol@bi.no.

Team

The AI Labor Market Index is a collaboration between the Frisch Centre and BI Norwegian Business School.

Øystein Hernæs

Øystein Hernæs

Senior Researcher, Frisch Centre

Andreas R. Kostøl

Andreas R. Kostøl

Associate Professor, BI Norwegian Business School

Download data

All series can be downloaded as CSV, in the same format as the DEL dashboard's data packages (plus an adjustment column). Current release: . Each package contains the time series, twelve-month change, annualized growth and a data dictionary.

The data may be used freely with attribution to The AI Labor Market Index (kiindeksen.no), see citation information. Underlying sources: the A-ordningen via microdata.no, Eloundou et al. (2024), Anthropic Economic Index, SSB.

Cite the AI Labor Market Index

Figures and numbers may be used freely with attribution. Here is how to cite the dashboard and the research paper:

The dashboard

Hernæs, Øystein and Andreas R. Kostøl (2026). The AI Labor Market Index: Artificial Intelligence and the Norwegian Labor Market. https://kiindeksen.no. Retrieved .

BibTeX
@misc{kiindeksen2026,
  author       = {Hern{\ae}s, {\O}ystein and Kost{\o}l, Andreas R.},
  title        = {The AI Labor Market Index: Artificial Intelligence and the Norwegian Labor Market},
  year         = {2026},
  howpublished = {\url{https://kiindeksen.no}},
  note         = {Retrieved {{DATO}}}
}

The research paper

Hernæs, Øystein and Andreas R. Kostøl (2026). “Does AI Widen Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway”. Working paper.

BibTeX
@unpublished{hernaeskostol2026ai,
  author = {Hern{\ae}s, {\O}ystein and Kost{\o}l, Andreas R.},
  title  = {Does AI Widen Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway},
  year   = {2026},
  note   = {Working paper}
}