The report Job Creation and Local Economic Development 2024: The Geography of Generative AI examines the health of regional labour markets and provides new estimates on regional labour shortages. In addition, it provides new findings on the impact of Generative AI on different regions and workers. It examines how AI technologies can be leveraged to address critical labour market challenges and boost productivity growth.
Job Creation and Local Economic Development 2024 - Country Notes: Norway
(a) Employment rate for the working age population (15-64 year-olds), 2023
Note: The figure shows the regional values and the national and OECD regional average in the working-age employment rate in 2023. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
In Norway the employment rate in 2023 varies across regions, ranging from a low of 75% in Agder and Sør-Østlandet to 78.6% in Western Norway. This represents a difference of 3.6 percentage points, below the average OECD regional dispersion of 10 percentage points. The national employment rate in Norway stands at 76.8%, above the OECD benchmark of 69.4%.
(a) Change in the employment rate, 2019 to 2023
Note: The figure shows the difference between 2019 and 2023 for the employment rate for regions in Norway, as well as the national and OECD regional average. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
By 2023, all of (6 out of 6) of Norwegian regions saw their employment recover to at least pre-pandemic levels. Trøndelag experienced the greatest recovery for employment rates, surpassing the pre-pandemic level by 3.3 percentage points. Overall, employment rates are 2.3 percentage points above pre-crisis levels, a stronger recovery than the regional OECD average of 1.5 percentage points.
(a) Change in the gap in the participation rate by age (between 25-64 year-olds and 15-24 year-olds) and gender (between men and women), 2013 to 2023 or closest available years
Note: The figure shows regional values and the national and OECD regional average in the change in the age gap (difference between the prime-age working population (25-64 year-olds) and youth (15-24 year-olds) and the gender gap (difference between men and women) in the participation rate in 2013 and that in 2023, or closest available years. The initial year refers to 2016 and the last year to 2023 for Oslo and Viken, Agder and Sør-Østlandet, Western Norway. A negative value implies that disparities decreased. The participation rate is defined as the number of employed persons and those looking for work as a share of the population in that subgroup. For gender disparities, it is defined using the working age population of 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Over the past ten years, the gap in participation rates between prime-age and younger workers (age inclusion gap) fell in all out of 6 regions in Norway, on average by 10.9 percentage points. The age inclusion gap grew by 1.3 percentage points across OECD regions. The biggest decrease in age disparities occurred in Western Norway at -12.4 percentage points, while the smallest decrease was in Trøndelag by -8.1 percentage points. Over the same period, the gap in participation rates between male and female workers (gender inclusion gap) fell in 3 out of 6 regions. The gender inclusion gap saw little change over this period. The biggest increase in gender disparities was in Innlandet by 4.6 percentage points, while the biggest decrease was in Northern Norway at -4.2 percentage points.
(a) Share of self-employed among all employed persons in the working-age population, 2022
Note: The figure shows the regional values and the national and OECD regional average in the share of self-employed among all working-age employed persons in 2022. The working-age population is defined as 15-64 year-olds.
Source: OECD calculations based on national labour force survey. For countries in the European Union, the survey is the EU-LFS.
In Norway self-employment levels stand at 5.1%, below the OECD benchmark of 15.5%. Innlandet has the highest share of self-employed workers at 5.4%. Western Norway, on the other hand, has the lowest share of self-employed workers at 3.4%.
(a) Share of youth not in employment, education or training among the youth working-age population, 2016
Note: The figure shows the regional values and the national and OECD regional average in the NEET rate (not in employment, education or training) for the youth working-age population (15-24 year-olds) in 2016.
Source: OECD calculations based on the OECD Regional databases.
In Norway, all (3 out of 3 regions with available data) have youth not in employment, education, or training (NEET) rates below the OECD benchmark of 16.8%, while the regional mean stands at 8%. The highest rate of youth exclusion is observed in Innlandet at 9.3%, while the lowest rate is in Trøndelag at 7.1%. This underscores the uneven opportunities for youth across the country.
(a) Labour productivity in USD 2015 PPP per worker, 2021
Note: The figure shows the regional values and the national and OECD regional average of labour productivity (USD 2015 PPP per worker) in 2021. The parentheses describe the difference in labour productivity relative to the national average. Labour productivity is defined as gross value added, measured in USD 2015 purchasing power parity, per worker.
Source: OECD calculations based on the OECD Regional databases.
(a) Ten-year annual labour productivity growth, 2012 to 2021
Note: The figure shows the regional values and the national and OECD regional average in the ten year annual growth rate of labour productivity (USD 2015 PPP per worker) from 2012 to 2021. Labour productivity is defined as gross value added, measured in USD 2015 purchasing power parity, per worker.
Source: OECD calculations based on the OECD Regional databases.
In 7 out of 7 regions in Norway, labour productivity is above the OECD benchmark. Jan Mayen and Svalbard leads labour productivity levels at 53% above the regional average. The lowest labour productivity is observed in Innlandet at -19% below the national average. Annual labour productivity growth in Norway over the past ten years is at 1.2%, above the OECD regional average of 0.9%. The strongest labour productivity growth is observed in Jan Mayen and Svalbard at 3.3% annual growth, and the weakest in Western Norway where labour productivity increased by 0.4% annually.
(a) Share of workers in low-, middle-, and high-skilled jobs, 2022
Note: The figure shows the share of workers in low-, middle- and high-skilled jobs for regions in Norway as well as the national and OECD regional average in 2022. Job skill is defined using ISCO occupational categories. Low skill corresponds to jobs in sales and services and un-skilled occupations (ISCO 5 and 9), medium-skilled workers hold jobs as clerks, craft workers, plant and machine operators and assemblers (ISCO 4, 7 and 8), and high-skilled workers are those who have jobs in managerial, professional, technical and associated professional occupations (ISCO 1, 2 and 3). The definition of skill is based on the educational level thought to be required of an occupation and does not consider skills not related to educational level.
Source: OECD calculations based on national labour force survey. For countries in the European Union, the survey is the EU-LFS.
In Norway, jobs requiring high skill levels dominate across all regions. Oslo and Viken stands out with the highest share of high-skill jobs (60.9%), above the OECD average of 44%. Innlandet has the highest proportion of medium-skill jobs, below the OECD benchmark of 30%. The share of low-skill jobs ranges from 20.8% in Oslo and Viken to 27.1% in Northern Norway, highlighting notable regional variation in job skill composition.
(a) Share of workers in mismatched jobs by over- and under-skilled, 2022
Note: The figure shows the regional values and the national and OECD regional average in the share of workers in mismatched jobs in 2022. Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECD’s Directorate for Employment, Labour and Social Affairs, whereby a worker is in a mismatched job when their educational skill level does not match the most common skill level of workers in that occupational group in that country. ‘Over-skilled’ means that the worker has an educational skill level above the most common educational skill level of their occupation. `Under-skilled’ means that the worker has an educational skill level below the most common educational skill level of their occupation.
Source: OECD calculations based on national labour force survey. For countries in the European Union, the survey is the EU-LFS.
Skill mismatches are less prevalent in Norway than in the OECD overall: 36% of workers are in jobs that do not match their educational skill level, compared to 35% across OECD regions. This ranges from 38% mismatched workers in Northern Norway to 34% in OECD.
(a) Differences of regional labour market tightness (vacancies over unemployment) relative to the national average, 2022.
Note: This figure shows the difference in labour market tightness between regions and the national level (in %) on the horizontal axis. Labour market tightness is defined as the number of vacancies over unemployment.
Source: Own elaboration based on Lightcast, EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.
In Norway, the extent of labour shortages varies by region. Taking labour market tightness (i.e. vacancies divided by unemployment), as a proxy, Western Norway is the region that faces the most severe labour shortages with 302% more vacancies per unemployed person than Norway as a whole. In contrast, Oslo and Viken is the region that experiences the least severe labour shortages, as it has 92% fewer vacancies per unemployed person than Norway on average.
The following tightness estimates for green and ICT jobs come with a small change in the methodology. Rather than dividing vacancies by employment—as done for the aggregate tightness estimates—tightness for green and ICT jobs is estimated as the ratio of vacancies to employment in each occupational group, as information on an unemployed person’s last job is not available in most countries.
(a) Difference between the labour market tightness of green jobs and the average job in the labour market by region (in %), 2022.
Note: This figure shows the difference between the labour market tightness of green jobs and the average job in the respective labour market (in %) on the horizontal axis. Labour market tightness is defined as the number of vacancies over employment. Occupations that have at least 10% of their tasks classified as green are defined as green jobs, following “Job Creation and Local Economic Development 2023: Bridging the Great Green Divide” (OECD 2023). The country average refers to the employment-weighted average of regions and the OECD average referes to the unweighted average of countries.
Source: Own elaboration based on Lightcast, EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), Australian Bureau of Statistics, and OECD (2023).
Norway experiences lower shortages for green jobs than for the average job. Specifically, there are on average -17% less vacancies per employed person in green jobs than for the average job in Norway compared to 29% in the OECD. Tightness among green jobs is highest in Trřndelag, where green jobs show 7% more vacancies per employed person, and lowest in Innlandet, where green jobs are -57% tighter than the average job.
(a) Difference between the labour market tightness of ICT jobs and the average job in the labour market by region (in %), 2022.
Note: This figure shows the difference between the labour market tightness of ICT jobs and the average job in the respective labour market (in %) on the horizontal axis. Labour market tightness is defined as the number of vacancies over employment. ICT specialists are defined as “workers who have the ability to develop, operate and maintain ICT systems, and for whom ICT constitute the main part of their job”, following Eurostat (Eurostat 2024). The country average refers to the employment-weighted average of regions and the OECD average referes to the unweighted average of countries.
Source: Own elaboration based on Lightcast, EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), Australian Bureau of Statistics, and Eurostat (2024).
Norway experiences higher shortages among ICT jobs than for the average job, as there are on average 62% more vacancies per employed person in ICT jobs than in the average job in Norway. This compares to 117% higher ICT tightness in the OECD. Tightness among ICT jobs is highest in Western Norway, where ICT jobs are 134% tighter than the average job, and lowest in Innlandet, where ICT jobs have -25% more vacancies per unemployed person.
AI has the potential to transform local labour markets by boosting productivity, creating or destroying jobs, and changing the very nature of some jobs, including job quality. While the full extent of its impact is still uncertain, the effects on jobs or skills will likely be context- and place specific. This report explores both the observed and anticipated impacts of technologies, both AI and non-AI, as they mature and achieve widespread adoption.
Narrow-purposed technologies in local labour markets
Even before the emergence of Generative AI, the impact of automation technologies differed across local labour markets. This measure of risk of automation serves as a useful metric to examine the effects of narrow-purposed technologies, these are, technologies (digital or not) that are intended to help with or take over one or a few specific tasks. The metrics presented below explore the share of jobs at risk of automation given available technologies at the end of 2021.
In Norway, on average around 2.6% of workers are considered at high risk of automation, meaning over 25% of its skills and abilities are highly automatable. This is 9.4 percentage points less than the OECD average of 12%. This figure ranges from 1.3% in Oslo and Viken to 4.9% in Innlandet.
(a) Share of employment at high risk of automation in OECD regions (2022)
Note: This figure shows the share of employment at high risk of automation in each region. Risk of automation is defined at the occupation level, where an occupation is considered exposed at high risk of automation if at least 25% of its skills and abilities are automatable. Averages represent the weighted regional average.
Source: OECD calculations based on Lassébie and Quintini, 2022[34], labour force survey and employment by occupation tables.
Regional employment exposed to Generative AI
(a) Share of employment exposed to Generative AI in TL-2 regions, 2022
Note: This figure shows the share of employment exposed to Generative AI in each region. Exposure is defined at the occupation level, where an occupation is considered exposed to Generative AI if at least 20% of its tasks can be done twice as fast with the help of Generative AI. OECD and country averages represent the weighted regional average.
Source: OECD calculations based on Eloundou et al., 2023[49], labour force survey and employment by occupation tables.
In Norway, on average around 28.2% of workers are exposed to Generative AI, meaning 20% (or more) of their job tasks could be done in half the time with the help of Generative AI. This is 2.2 percentage points more than the OECD average of 26%. This figure ranges from 17.2% in Innlandet to 34.7% in Oslo and Viken.
(a) Share of employment exposed to Generative AI and at high risk of automation , 2022
Note: This figure shows the share of employment exposed to Generative AI and the share of employment at high risk of automation in each region in each region. Exposure is defined at the occupation level, where an occupation is considered exposed to Generative AI if at least 20% of its tasks can be done twice as fast with the help of Generative AI while an occupation is considered at high risk if at least 25% of its skills and abilities are automatable. Horizontal and vertical lines represent the country average.
Source: OECD calculations based on Eloundou et al., 2023[49], labour force survey and employment by occupation tables.
OECD regions previously only mildly at risk of automation are now significantly exposed to Generative AI and vice versa. There tends to be a negative correlation between the share of exposed workers to Generative AI and a region’s share of workers at high risk of automation.
The concentration of industries within or outside cities drives disparities in Generative AI exposure between urban and non-urban labour markets. Certain industries, such as financial services or technology development, often concentrate around metropolitan areas while non-metropolitan or rural areas tend to rely on industries with a different production structure, such as agriculture or manufacturing. Similarly, workers are also spatially concentrated with highly skilled workers often being more present in clusters in or around a few metropolitan areas.
(a) Share of employment exposed to Generative AI by area type in Norway (2022)
Note: This figure shows the share of employment exposed to Generative AI in different types of areas. Exposure is defined at the occupation level, where an occupation is considered exposed to Generative AI if at least 20% of its tasks can be done twice as fast with the help of Generative AI.
Source: OECD calculations based on Eloundou et al., 2023[49], labour force survey and employment by occupation tables.
The share of workers exposed to Generative AI is larger in cities compared to rural areas by 19.3 percentage points, which makes cities 2 times more exposed than non-urban areas. This gap is larger than average as across OECD countries urban areas are 1.8 times more exposed than non-urban areas.
References
OECD (2024), Job Creation and Local Economic Development 2024: The Geography of Generative AI https://doi.org/10.1787/83325127-en
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