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: Slovenia
(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 Slovenia the employment rate in 2023 varies across regions, ranging from a low of 71.2% in Eastern Slovenia to 74% in Western Slovenia. This represents a difference of 2.8 percentage points, below the average OECD regional dispersion of 10 percentage points. The national employment rate in Slovenia stands at 72.6%, 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 Slovenia, 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 (2 out of 2) of Slovenian regions saw their employment recover to at least pre-pandemic levels. Eastern Slovenia experienced the greatest recovery for employment rates, surpassing the pre-pandemic level by 0.8 percentage points. Overall, employment rates are 0.6 percentage points above pre-crisis levels, a weaker 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
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. 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) increased in all out of 2 regions in Slovenia, on average by 2.7 percentage points. The age inclusion gap grew by 1.3 percentage points across OECD regions. The smallest increase in age disparities occurred in Western Slovenia at 2.2 percentage points, while the biggest increase was in Eastern Slovenia by 3.1 percentage points. Over the same period, the gap in participation rates between male and female workers (gender inclusion gap) fell in 2 out of 2 regions. The gender inclusion gap fell by, on average, 1.4 percentage points. The smallest decrease in gender disparities was in Western Slovenia by -1.1 percentage points, while the biggest decrease was in Eastern Slovenia at -1.7 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 Slovenia self-employment levels stand at 12%, below the OECD benchmark of 15.5%. Western Slovenia has the highest share of self-employed workers at 13.2%. Eastern Slovenia, on the other hand, has the lowest share of self-employed workers at 10.9%.
(a) Share of youth not in employment, education or training among the youth working-age population, 2022
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 2022.
Source: OECD calculations based on the OECD Regional databases.
In Slovenia, all (2 out of 2 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 9%. The highest rate of youth exclusion is observed in Eastern Slovenia at 10.2%, while the lowest rate is in Western Slovenia at 7%. This underscores the uneven opportunities for youth across the country.
(a) Labour productivity in USD 2015 PPP per worker, 2022
Note: The figure shows the regional values and the national and OECD regional average of labour productivity (USD 2015 PPP per worker) in 2022. 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 2022
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 2022. 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 0 out of 2 regions in Slovenia, labour productivity is above the OECD benchmark. Western Slovenia leads labour productivity levels at 8% above the regional average. The lowest labour productivity is observed in Eastern Slovenia at -8% below the national average. Annual labour productivity growth in Slovenia over the past ten years is at 1.3%, below the OECD regional average of 0.9%. The strongest labour productivity growth is observed in Western Slovenia at 1.3% annual growth, and the weakest in Eastern Slovenia where labour productivity increased by 1.2% 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 Slovenia 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 Slovenia, jobs requiring high skill levels dominate across all regions. Western Slovenia stands out with the highest share of high-skill jobs (53%), above the OECD average of 44%. Eastern Slovenia has the highest proportion of medium-skill jobs, above the OECD benchmark of 30%. The share of low-skill jobs ranges from 21% in Western Slovenia to 23.7% in Eastern Slovenia, 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 Slovenia than in the OECD overall: 25% of workers are in jobs that do not match their educational skill level, compared to 35% across OECD regions. This ranges from 34% mismatched workers in OECD to 25% in Eastern Slovenia.
(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 Slovenia, the extent of labour shortages varies by region. Taking labour market tightness (i.e. vacancies divided by unemployment), as a proxy, Western Slovenia is the region that faces the most severe labour shortages with 19% more vacancies per unemployed person than Slovenia as a whole. In contrast, Eastern Slovenia is the region that experiences the least severe labour shortages, as it has 16% fewer vacancies per unemployed person than Slovenia on average.
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 Slovenia, on average around 12.5% of workers are considered at high risk of automation, meaning over 25% of its skills and abilities are highly automatable. This is 0.5 percentage points more than the OECD average of 12%. This figure ranges from 6.7% in Central Slovenia to 22.2% in Carinthia.
(a) Share of employment at high risk of automation in OECD regions (2023)
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-3 regions, 2023
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 Slovenia, on average around 27.2% 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 1.2 percentage points more than the OECD average of 26%. This figure ranges from 19% in Carinthia to 36.4% in Central Slovenia.
(a) Share of employment exposed to Generative AI and at high risk of automation , 2023
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.
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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