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: Lithuania
(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 Lithuania the employment rate in 2023 varies across regions, ranging from a low of 70.7% in Central and Western Lithuania to 78.7% in Vilnius Region. This represents a difference of 8 percentage points, below the average OECD regional dispersion of 10 percentage points. The national employment rate in Lithuania stands at 74.7%, 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 Lithuania, 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, half of (1 out of 2) of Lithuanian regions saw their employment recover to at least pre-pandemic levels. In Lithuania and Vilnius Region employment did not return to pre-crisis levels. Central and Western Lithuania experienced the greatest recovery for employment rates, surpassing the pre-pandemic level by 0.4 percentage points. Overall, employment rates are 0.1 percentage points below 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) fell in 1 out of 2 regions in Lithuania, on average by 0.3 percentage points. The age inclusion gap grew by 1.3 percentage points across OECD regions. The biggest decrease in age disparities occurred in Central and Western Lithuania at -1.8 percentage points, while the biggest increase was in Vilnius Region by 1.2 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, 2.5 percentage points. The smallest decrease in gender disparities was in Central and Western Lithuania by -2.4 percentage points, while the biggest decrease was in Vilnius Region at -2.5 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 Lithuania self-employment levels stand at 11.3%, below the OECD benchmark of 15.5%. Central and Western Lithuania has the highest share of self-employed workers at 12.4%. Vilnius Region, on the other hand, has the lowest share of self-employed workers at 10.2%.
(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 1 out of 2 regions in Lithuania, labour productivity is above the OECD benchmark. Vilnius Region leads labour productivity levels at 19% above the regional average. The lowest labour productivity is observed in Central and Western Lithuania at -19% below the national average. Annual labour productivity growth in Lithuania over the past ten years is at 1.9%, above the OECD regional average of 0.9%. The strongest labour productivity growth is observed in Vilnius Region at 2.1% annual growth, and the weakest in Central and Western Lithuania where labour productivity increased by 1.7% 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 Lithuania 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 Lithuania, jobs requiring high skill levels dominate across all regions. Vilnius Region stands out with the highest share of high-skill jobs (61.5%), above the OECD average of 44%. Central and Western Lithuania has the highest proportion of medium-skill jobs, above the OECD benchmark of 30%. The share of low-skill jobs ranges from 16.7% in Vilnius Region to 25% in Central and Western Lithuania, 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 Lithuania than in the OECD overall: 20% 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 16% in Vilnius Region.
(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 Lithuania, the extent of labour shortages varies by region. Taking labour market tightness (i.e. vacancies divided by unemployment), as a proxy, Vilnius Region is the region that faces the most severe labour shortages with 91% more vacancies per unemployed person than Lithuania as a whole. In contrast, Central and Western Lithuania is the region that experiences the least severe labour shortages, as it has 29% fewer vacancies per unemployed person than Lithuania 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).
Lithuania experiences higher shortages for green jobs than for the average job. Specifically, there are on average 2% more vacancies per employed person in green jobs than for the average job in Lithuania compared to 29% in the OECD. Tightness among green jobs is highest in Central and Western Lithuania, where green jobs show 4% more vacancies per employed person, and lowest in Vilnius Region, where green jobs are -9% 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).
Lithuania experiences higher shortages among ICT jobs than for the average job, as there are on average 91% more vacancies per employed person in ICT jobs than in the average job in Lithuania. This compares to 117% higher ICT tightness in the OECD. Tightness among ICT jobs is highest in Central and Western Lithuania, where ICT jobs are 118% tighter than the average job, and lowest in Vilnius Region, where ICT jobs have 44% 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 Lithuania, on average around 9.2% of workers are considered at high risk of automation, meaning over 25% of its skills and abilities are highly automatable. This is 2.8 percentage points less than the OECD average of 12%. This figure ranges from 6.1% in Vilnius Region to 10.8% in Central and Western Lithuania.
(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 Lithuania, on average around 27% 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 percentage points more than the OECD average of 26%. This figure ranges from 21.6% in Central and Western Lithuania to 37.2% in Vilnius Region.
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 Lithuania (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 16.1 percentage points, which makes cities 1.8 times more exposed than non-urban areas. This gap is similar to the 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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