Job Creation and Local Economic Development 2024

2. Labour shortages across regional labour markets

Abstract

Many OECD regions struggle with significant labour shortages that hold back economic growth. This chapter presents new data and analysis on labour market tightness across OECD regions. It examines some of the drivers of labour shortages and explores which sectors and jobs face the greatest labour shortages in different regions. It also considers the shortages for green and Information and communication technologies (ICT) jobs, which could hold back progress in the twin transition for many regions. Finally, this chapter analyses how and where current population trends of demographic change could intensify regional labour shortages.

In Brief

Driven by record-high employment rates, population ageing, new skills requirements, and changing worker preferences, most OECD regions are struggling with labour and skills shortages.

Labour markets across OECD regions have more than bounced back from the COVID-19 pandemic. Employment levels are record high in many countries. There are also increasing demands for new types of skills across many sectors, including those for advanced AI and “green-task” jobs. As a result, firms now face increasing difficulties in finding and hiring the right workers to fill vacancies. The resulting labour and skills shortages are a potential drag on firm operations, productivity, and economic growth, as well as producing flow-on consequences for the supply of critical public services.

• Regional labour shortages have risen substantially since 2019 and increasingly also affect regions with previously low levels of labour shortages. Regional labour market tightness, defined as vacancies per employed person, has increased substantially since 2019. Data from the United States and Germany show that regional labour market tightness has increased by 50% and 80%, respectively, between 2019 and 2022, affecting all types of regions.

• The severity of rising labour shortages differs a lot across regions. In the United States, for example, tightness grew by 29% and 105% in the 10% of regions with the lowest and the highest increases on average. While the growth in labour shortages seems to have slowed in most OECD regions since 2021, shortages remain at a high level relative to pre-COVID-19 levels (e.g. 50% higher in the United States), highlighting that the drivers of labour shortages go beyond the temporary post-COVID-19 recovery.

• Regional disparities in labour shortages are significant across the OECD. Within countries, the tightest regional labour market reports on average five times higher labour market tightness than the least tight region. Regions with a large population and high employment rates (relative to the national average) experience 14% and 26% higher shortages relative to low-population and low-employment places, respectively. Labour shortages are also more acute in regions that rely more on tradable services or that have benefitted from the presence of high-growth industries.

• In 95% of regions, labour shortages in ICT are higher than for other jobs, with on average over twice as high labour market tightness. Similarly, labour shortages are more pronounced for green jobs in 89% of regions. On average, labour shortages are between 40% (European regions) and 15% (Australian regions) higher for green than non-green jobs. Greater shortages in green and digital jobs partly reflect the transformation of regional economies in the twin transition but could also indicate skills mismatches, as education and training systems have not yet fully adapted to new labour market demand. Furthermore, regions with a 10% higher green jobs tightness also show an 18% higher ICT job tightness, suggesting that the digital and green transitions impact many places simultaneously.

• Population ageing risks exacerbating labour shortages to varying degrees across regions. If current population trends continue, regional labour shortages could increase by almost 9% by 2042, and almost twice as much (16%) in the oldest 20% of OECD regions. Consequently, tightness is projected to increase from one vacancy for every 21 working age persons to one vacancy for every 18 working age persons in the oldest 20% of regions.

• Policies designed to mitigate labour shortages need to reflect place-specific challenges, which include retaining and attracting (young) talent to remote regions and facilitating job transitions, taking into account the geographic distribution of jobs. Labour market intelligence tools can inform the design of such place-based policies, which requires more detailed employment data, for example at the geographic and occupational level.

Introduction

Labour shortages are a major challenge for firms and for economic development more broadly across the OECD. More than half (54%) of all SMEs in the European Union report difficulties in finding employees with the right skills – the most commonly nominated among a range of business problems (European Commission, 2023[1]). Although difficult to quantify, this lack of adequately skilled employees represents a drag on firm operations, negatively impacting innovation, productivity and economic growth. While labour shortages have somewhat attenuated since the post-COVID recovery (OECD, 2024[2]), a substantial skills mismatch persists in the labour market (Chapter 1). Tensions are particularly significant in occupations that are crucial for the green and digital transitions, representing bottlenecks on the path to achieving net-zero emission goals. Regions will need to revisit policies and tools they have at their disposal, including their skills training systems, support for the economically inactive, (im)migration policies, and labour market intelligence to address these labour and skills shortages.

While labour shortages have eased after the marked increase due to COVID-19, national labour markets remain tight (Figure 2.1). By the end of 2022, there were about 0.6 vacancies per unemployed person on average for OECD countries with available data, down from its peak of 0.65 in 2021 but still significantly above the pre-crisis average of 0.4. The United States stands out with the greatest labour market tightness of about 1.8 vacancies per unemployed person, down from almost two vacancies per unemployed person at its peak. Still, labour market tightness is 55% higher than before the COVID-19 crisis, indicating that labour shortages persist. The latest increase in labour market tightness is especially pronounced in some countries. In Australia, for example, vacancies per unemployed persons tripled compared to pre-crisis levels. For Luxembourg and Norway, that figure doubled.

Figure 2.1. Labour markets are tight in many countries, despite some signs of easing post-2020

Number of vacancies per unemployed person. National definitions, seasonally adjusted.

Note: This figure is taken from (OECD, 2023[3]). OECD is an unweighted average of the countries shown above. In Panel A, the definition of vacancies is not harmonised across countries. See figure 1.7 in (OECD, 2023[3]) for details.

Source: OECD (2020), “Labour: Registered unemployed and job vacancies (Edition 2019)”, Main Economic Indicators (database), https://doi.org/10.1787/190bb5bc-en (accessed on 23 June 2023) for Australia, Austria, Germany, Hungary, Portugal, the United Kingdom, Job vacancy statistics by NACE Rev.2 activity for Finland, Luxembourg, Latvia, Lithuania, the Netherlands, Norway, Poland, the Slovak Republic, Slovenia and Sweden (Eurostat), Job vacancies, payroll employees, and job vacancy rate (Statistics Canada), Les demandeurs d’emploi inscrits à Pôle emploi (Dares, France), Posti vacanti (Italian National Institute of Statistics), Job Openings and Labor Turnover Survey (U.S. Bureau of Labor Statistics, retrieved from FRED); Online job posting on Indeed.

Labour shortages are widespread, and the main drivers are a combination of cyclical and structural factors, with the latter being the larger driver in European countries. Cyclical factors reflect the reprise of economic activity after downturns to which the supply of workers does not adjust quickly enough. Structural factors arise from longstanding mismatches in terms of skills and preferences (e.g. working conditions) between jobseekers and employers. The decline in the working-age population and an insufficient supply of workers with highly specialist skills, such as STEM, are important contributing factors. Furthermore, the ongoing green and digital transitions are placing additional pressure on shortages (European Labour Authority, 2022[4]). This is in line with evidence that firms with higher skill requirements and fast-growing innovative firms are more likely to experience labour shortages (Groiss and Sondermann, 2023[5]).

While labour shortages were mostly limited to high-skilled occupations over the past decades, previous research found that they affect both high-skilled and lower-skilled, contact-intensive occupations since the COVID-19 pandemic (Causa et al., 2022[6]). This is due in part to workers shifting away from jobs with poor working conditions, such as low-paying and contact-intensive ones (Zwysen, 2023[7]). However, country-specific factors exist, as this shift is observed in the US and the UK but not in Germany (OECD, 2024[2]). Another reason for shortages spreading to different types of occupations is the relatively small pool of unemployed persons seeking employment (due to high employment rates) during the post-COVID recovery. A strong labour market is likely to further aggravate shortages by encouraging workers to quit their jobs as a tight labour market facilitates the job search (OECD, 2024[2]). Additionally, labour hoarding, which occurs when companies retain workers without fully utilising their capacity, contributed to the resilience of the European labour market despite weak economic growth throughout 2022 and 2023 (Gayer et al., 2024[8]). Yet, it can also aggravate labour shortages, by preventing workers from moving to recruiting firms (Doornik, Igan and Kharroubi, 2023[9]).

This chapter aims to fill the knowledge gap on labour market tightness in regional labour markets, as well as at the occupational and industry level. Country-level estimates mask regional differences and effective policy measures depend on a local labour market perspective. Therefore, this chapter zooms in on OECD regions and analyses the geography of labour market tightness. Additionally, it presents new estimates on labour market tightness for different sectors and occupations, which helps to shed light on which regional economies are driving labour shortages and therefore most in need of policy support. The possible additional impact of ageing populations on labour shortages is also explored. The chapter concludes with a discussion of potential policy levers to alleviate labour shortages, such as the increased use of technology, increasing participation of hard-to-reach groups, labour market intelligence tools, skills training, and (im)migration policies.

Workers’ geographic mobility is more likely to impact estimates of regional (compared to national) labour market tightness and, consequently, their interpretation. Measures of labour market tightness implicitly assume that vacancies can only be filled by jobseekers within the same geographic unit, making the geographic level of analysis an important methodological choice. Ideally, the geographic level would coincide with functional labour markets, within which workers commute between their place of residence and their workplace (i.e., no mobility between regions). However, due to data limitations, this chapter uses the first administrative tier of subnational governance (i.e., TL2, corresponding to states in the United States), whenever subnational employment data are available. While TL2 regions are relatively large such that, in many cases, workers stay within the same TL2 region, labour markets can span multiple TL2 regions and multiple labour markets may exist in one TL2 region, resulting in an inaccurate picture of labour market tightness. This caveat, particularly relevant for TL2 regions with high inter-region mobility, needs to be kept in mind when interpreting regional tightness estimates. More detailed geographic employment data would mitigate this shortcoming by enabling the analysis at the level of functional labour markets.

This chapter’s tightness estimates do not allow for comparisons across regions in different countries due to data limitations in online job postings data (Box 2.3 and Box 2.4). Online job postings provide detailed and timely data on labour demand. However, there exist differences across countries in terms of their overall coverage, as well as the representation of specific industries, occupations and regions (Box 2.4). Therefore, this chapter presents tightness measures relative to the country average for aggregate regional estimates, and relative to the regional average for occupational (or industry) breakdowns.

Disparities in labour market tightness across regions remain large despite widespread increases in recent years

In the vast majority of OECD countries, regions face substantially different degrees of labour shortages. Based on the relative labour market tightness indicator (see Box 2.3 for methodology), the tightest region is on average more than five times tighter than the least tight region within a country (Figure 2.2). The country with the greatest regional dispersion is Italy, where the greatest relative tightness observed in the Bolzano-Bozen Province is over four times the national average and the region with the lowest tightness is less than one-seventh of the national average. Norway also displays high dispersion: Western Norway (the tightest region) shows tightness of almost four times the national average, while the lowest tightness is evident in Oslo and Viken at 0.07 times the national average. These regions with high relative tightness potentially reflect low unemployment rates as in Bolzano-Bozen Province (2.3% in 2022) or the concentrated nature of each regional economy: offshore oil in Western Norway and tourism in the Ionian Islands. The lowest regional dispersion is found in Finland.

Figure 2.2. Labour markets are tight across OECD regions, with large dispersion in over half of countries

Regional labour market tightness relative to national average, 2022.

Note: Relative labour market tightness at the regional level is the number of vacancies over unemployment for a given region, divided by the national labour market tightness average. The horizontal axis shows the average relative labour market tightness across regions. Regions with a population below 10 000 are dropped. The sample includes all OECD countries with available data, including the OECD accession countries of Bulgaria, Croatia and Romania.

Source: OECD elaboration based on OECD regional database for unemployment data, and Lightcast data for vacancies.

Labour shortages are often concentrated in specific communities that drive a high labour market tightness in OECD countries. In 10 out of 26 countries, the median of regional relative labour market tightness is below the national average, which is normalised to one. In these countries, most regions display low tightness and so, there are few regions with high labour market tightness within the country. In addition, capital regions do not stand out in terms of tightness: they are the tightest region only in five out of the 26 countries with data. This may partly be because in some countries urban regions are, surprisingly, underrepresented in online job vacancy data (Box 2.4).

To estimate labour market tightness at a detailed occupational and industry level, the remainder of this chapter measures tightness as the ratio of online job vacancies to employment, rather than unemployment. The reason for this is that most countries do not provide information on the unemployed persons’ last occupation, which would be required to construct occupation-level tightness estimates using the standard tightness measure. Nevertheless, Box 2.1 shows that both labour market tightness measures, namely the one using the standard unemployment-based definition and this chapter’s employment-based definition, align well across regions.

Box 2.1. How well do different measures of labour market tightness align?

Standard measures of labour market tightness divide vacancies by unemployment. To provide tightness estimates at the occupational level, this chapter uses employment instead of unemployment, as information on unemployed individuals’ last occupation is not available for most countries. This box assesses the degree of alignment between the two measures, one based on unemployment and the other on employment.

Figure 2.3. Employment and unemployment-based measures lead to similar tightness results

Regional tightness relative to the national level (=100) using employment (horizontal axis) and unemployment (vertical axis) in the denominator, 2022.

Note: labour market tightness on the horizontal axis is computed by dividing online vacancies by employment at the regional level, while the vertical axis reports tightness based on a measure that uses unemployment in the denominator. The 45°-line indicates where both measures produce identical results.

Source: Own elaboration based on Lightcast, OECD regional unemployment statistics, and labour force surveys: EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Both the unemployment-based (vertical axis) and the employment-based (horizontal axis) relative tightness measures provide similar results (Figure 2.3). In most cases regional tightness, relative to the national average, falls close to the 45°-line, on which both measures are identical. The similarity of the two measures is confirmed by a high correlation of 0.79. This suggests that this chapter’s employment-based tightness measure is also an appropriate proxy for labour shortages.

Regional labour markets have witnessed a notable increase in tightness since 2019, a trend especially exacerbated by the COVID-19 crisis. Figure 2.4 presents an illustrative example using regions in the United States, where Lightcast data is best comparable since 2019, but similar trends are observed across OECD countries (OECD, 2023[3]). Between 2019 and 2022, regional labour market tightness in the United States increased by over 50%. The increase is especially pronounced during the COVID-19 crisis. Between 2020 and 2021, the regional median increased by an additional thirty percentage points, relative to 2019, in contrast to the approximately 10 percentage point increases observed in the preceding and subsequent years.

While regions still face widespread labour shortages, the growth of labour market tightness is showing signs of slowing down after the initial effect of the COVID-19 pandemic. Both regions with the most and the least severe shortages show a slower increase in labour market tightness since 2021. However, by the end of 2022, tightness was still increasing at a relatively high rate across all regions, standing at more than 10 percentage points.

Figure 2.4. Labour markets have become tighter — across all regions

Evolution of relative labour market tightness in US regions indexed to 2019 (= 100), 2019 to 2022.

Note: Illustration based on the United States. Absolute tightness is indexed to the year 2019 (=100). The figure shows the evolution of absolute labour market tightness for US regions in the top 20%, bottom 20% and the regional median of absolute tightness. The group of regions in the top and bottom 20% each account for at least 20% of the employed population in the region.

Source: Own elaboration based on Lightcast vacancies and Bureau of Labour Statistics (USA).

Box 2.2.  Shortages vs. dynamism: disentangling drivers of labour market tightness

Rising labour market tightness cannot be explained through increasingly dynamic labour markets. If workers are entering and leaving their jobs at a more frequent rate, it is a sign of a vibrant labour market, since workers can quickly transition to new jobs. Figure 2.5 plots the rate at which workers start new jobs and leave their jobs in European regions. Based on this evidence, labour markets have not become more dynamic: besides the dip in new hires and rise in separations in 2020, the trend is flat. This contrasts with labour market tightness, which increased sharply in 2021 and continues to increase into 2022 (Figure 2.4). Therefore, the measure of labour market tightness presented in this chapter is more likely to capture actual labour shortages than dynamic labour markets, and the two terms are used interchangeably throughout this note.

Figure 2.5. Job switches do not explain increasing labour market tightness

New hires and job separation rates over all employed persons, 2017 to 2022.

Note: Illustration based on European countries. New hires rate is defined as the number of people who stated they started a new job in that year over the employed population. The turnover rate is defined as the number of people who stated they left their job that year over the employed population. The figure shows the evolution of the new hires (left) and turnover rate (right) for regions in the top 20%, bottom 20% and the regional median of absolute tightness. The group of regions in the top and bottom 20% each account for at least 20% of the employed population in the region.

Source: OECD elaboration based on EU-LFS, including the OECD accession countries of Bulgaria, Croatia and Romania.

The extent of labour shortages depends on the characteristics of the regional economy

This section examines to what extent labour shortages depend on the regions’ demographic composition, labour market conditions and economic structure. To do this, it analyses the relation between labour market tightness (relative to the national level) and regional characteristics in linear regression models (Figure 2.6).

Tightness differs by regional demographic and employment characteristics

More populous and urban regions face more intense labour shortages. The 50% of regions with the highest population in a country (i.e. those above the population median) have 14% higher tightness levels than those below the median (Figure 2.6). Similarly, regions with above-median population density and projected labour force growth have on average 26% and 15% higher tightness levels, respectively, compared to those below the median. Labour demand is likely strongest in more urban areas, to which the labour force adapts by relocating to such high labour demand areas.

Regions with high employment rates face more severe labour shortages. Labour market tightness is on average 19% higher in regions with employment rates above the national median, relative to those with below-median employment rates. This aligns with the interpretation of high employment rates being a driver of labour shortages, as the pool of job seekers is relatively small. Additionally, labour shortages are 20% lower in regions with above-median employment growth over the past five years, potentially because employment growth is more likely to be observed in regions with relatively low employment rates and low levels of tightness. The negative association between employment growth and tightness is also intuitive as firms in regions with a growing labour market are able to fill vacancies and therefore experience lower labour shortages. An important caveat is that the association between labour market tightness and employment rate growth is partially mechanical since a higher number of filled positions decreases labour market tightness according to this chapter’s definition.

Figure 2.6. Labour shortages are higher in regions that are more urban, have high employment rates, and rely more on tradable services

Average difference in labour market tightness (relative to the national level) between regions with high levels (i.e. above median) and low levels (i.e. below median) of the characteristics reported on the horizontal axis.

Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. Each bar corresponds to the coefficient of a univariate, regional-level regression of labour market tightness (relative to the national average) on the respective characteristic on the horizontal axis. In each regression, the covariate takes the form of a binary variable indicating that a region is above the country-specific median value of the respective characteristic (e.g. population). Thus, the vertical axis reports the mean difference in labour market tightness between regions above the country-specific average (e.g. in population) relative to those below the country-specific average. The characteristics are defined (from left to right) as a region’s working-age population, population density, its projected labour force change over the future 10 years, the level and the 5-year change in its employment to working-age population rate, the employment share in tradeable services (ISIC broad sectors G to N) and tradeable goods (ISIC sectors B, D, E). Standard errors are clustered at the country level. The regressions include all regions in countries with at least two subnational regions with available data, namely Australia, Belgium, Switzerland, Czechia, Germany, Denmark, Greece, Spain, Finland, France, Hungary, Ireland, Italy, Lithuania, Norway, Poland, Portugal, Sweden, Slovenia, Slovakia, the United Kingdom and the United States.

Source: OECD elaboration based on Lightcast, the OECD Region and Cities databases and labour force surveys: EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

The regional economic structure also matters for the extent of shortages

Regions that are more specialised in tradeable services experience higher labour shortages. Regions with an above-median employment share in tradeable services industries (i.e. ISIC broad groups G to N) experience 21% higher tightness levels compared to those below median.1 A likely explanation is that some tradeable services industries have seen particularly high and growing demand, for example ICT as a central industry to the twin transition or accommodation and food service activities in the post-COVID recovery. Additionally, it is important to note that office-based jobs – many of which are part of tradeable services industries – are overrepresented in online vacancy data in many countries (Box 2.4), which might overstate the statistical relation between labour market tightness and tradeable services to some degree. Regions with a high share of employment in tradeable goods industries do not show tighter labour markets than those with low employment shares in these industries.

Box 2.3. Labour market tightness as a proxy for labour shortages

Labour market shortages occur when firms are not able to fill open positions. As firms cannot hire the desired employees, labour shortages may impede economic growth and productivity, both for individual firms and the economy as a whole. Despite its importance for the monitoring of a region’s economic health, labour market shortages are difficult to measure since standard labour market statistics do not typically track job vacancies (and whether these are filled) in a consistent manner across time and countries. Throughout this note, labour market tightness is used as a proxy for labour shortages.

Methodology

Labour market tightness is calculated as the number of job vacancies over the number of employed persons in a given region and year. The measure is calculated for different occupations and industries in a given region as:

${labour market tightness}_{r,t}= \frac{{Online\mathrm{ }job\mathrm{ }postings}_{r,t}}{{Employment}_{r,t}}\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{ }\mathrm{r}\mathrm{e}\mathrm{g}\mathrm{i}\mathrm{o}\mathrm{n} r \mathrm{a}\mathrm{n}\mathrm{d}\mathrm{ }\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r} t.$

The number of job vacancies comes from online job postings collected by Lightcast while employment data are derived from labour force surveys and national statistics (see notes). This indicator differs from other approaches in that it divides the number of job vacancies by employment (rather than unemployment) (OECD, 2023[3]). However, using employment in the denominator allows for a more accurate disaggregation by occupation and industry, as employed individuals can be attributed to a specific occupation and industry. This results in more detailed information on which sectors or occupations experience greater labour market shortages. Figure 2.3 shows that the standard unemployment-based and the employment-based measure used in this chapter align well across regions.

This chapter presents labour market tightness over time (i.e. 2019-2022), for subnational regions (mostly TL2), as well as by occupation and industry. Since the data quality of online job postings (i.e. how accurately it tracks all job openings in an economy) varies by country and over time, this note reports relative measures of tightness. Specifically, the results report occupation- and industry-level estimates relative to the region’s average level of tightness, while regional estimates (i.e. for all occupations and industries) are presented relative to the national tightness level. Thus, this approach only allows for comparisons of tightness levels between regions relative to their country’s average, rather than in absolute terms. Similarly, occupation- and industry-level tightness estimates can only be compared across regions in relation to their regional tightness average. Additionally, the evolution over time is only reported for the United States (Figure 2.4), as its data is the most reliable across time.

However, some challenges arise when proxying labour shortages with labour market tightness. First, the labour market tightness measure does not account for whether a vacancy is filled. Theoretically, both actual labour shortages and a dynamic labour market (with many vacancies that are filled) could lead to high labour market tightness index. Second, the regional, occupational, and sectoral representativeness of online job postings data varies by country (Box 2.4). This chapter mitigates these shortcomings by restricting the results to where the data are reliable and by reporting results in relative terms.

Notes: Job vacancies are provided by Lightcast; employment data come from EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

High-skilled occupations mainly drive regional labour shortages

Within regions, labour market tightness varies to a large degree by occupation. The tightest ten occupations in Europe and the United States have between two and almost five times more job openings per employed person in that occupation than the average job in that region (Figure 2.7). Furthermore, the tightest occupation in Europe (Sales Marketing and Development Managers) is among the five tightest occupations in 42% of regions, highlighting that many regions face shortages in that occupation. The same is true for 42% of regions when it comes to software and applications developers and analysts, and 35% of regions in the case of transport and storage labourers.

Box 2.4. How representative are online job postings data?

Vacancy data from online job postings (OJPs) provide timely and detailed information. However, they also come with differences in terms of representativeness across countries, regions within countries, occupations and industries. This box provides a brief overview of the representativeness of OJP data from Lightcast (LC) in countries included in this chapter. Existing work in this area compares LC data to official data from national statistical offices and labour agencies, which come with their own limitations (e.g. if firms report vacancies voluntarily). Thus, these comparisons provide a measure of similarity between available sources, rather than an absolute measure of representativeness.

For Anglophone countries, namely the United States, the United Kingdom, and Australia, LC data match official statistics relatively well when considering the distribution of vacancies (e.g. the share of vacancies in a specific region, occupation or industry) (Tsvetkova et al., 2024[10]). Since the total number of vacancies in LC data is consistently below the one in national statistics, this chapter refrains from reporting absolute levels of tightness (i.e. the number of OJPs per filled position). Nevertheless, differences in the distribution of vacancies do exist. Overrepresentation is highest for the education and health sectors, whose share is between 12 and 15 percentage points higher in LC than in official statistics. Evidence of occupational representation is limited to Canada, where management and professional occupations are overrepresented and lower skilled occupations are underrepresented. Importantly, these sectoral and occupational patterns remain relatively stable between 2015 and 2022. The regional representation is in general high for Anglophone countries, showing differences of one percentage point at most between LC data and official records with very few exceptions.

The representativeness of LC data varies strongly across European countries (Vermeulen and Gutierrez Amaros, 2024[11]). In 2020, the ratio of OJPs to those reported in official statistics ranged from 6.8 in Ireland to 0.4 in Czechia. Between 2019 and 2022, the coverage of OJPs increased substantially such that in 2022 the number of OJPs exceeds the ones from official statistics in all countries except Luxembourg. Consequently, this chapter does not report time trends for European countries. Subnational regions are not equally represented in most countries, with the exception of Sweden and the Netherlands. Surprisingly, urban areas are not always overrepresented, as capital regions are underrepresented in Belgium, Hungary and Romania. Regarding industries, manufacturing, utilities, ICT, and finance and insurance activities are in general overrepresented. Furthermore, professional and administrative occupations tend to be overrepresented, potentially because office-based job vacancies are more likely to be posted online. It is important to take these results into account when interpreting this chapter’s findings.

Source: (Vermeulen and Gutierrez Amaros, 2024[11]); (Tsvetkova et al., 2024[10])

High-skilled occupations are in demand across OECD regions

Labour shortages in OECD regions affect high-skilled occupations more than low- and medium-skilled occupations. In two-thirds of European regions and all regions in the United States, high-skilled jobs, namely managers and professionals (i.e. defined at the 1-digit level of the European occupational classification ISCO), are the tightest occupational group. Their average tightness is 25% and 41% higher than the average job in the labour market in Europe and the United States, respectively. Low-skilled jobs, for example, service and sales workers, are the second tightest skilled group, with 2% and 32% lower-than-average tightness levels in Europe and the United States, followed by medium-skilled jobs (22% and 34% lower-than-average tightness). These findings contrast evidence on increased labour shortages among low-and medium-skilled occupations, which may partly be explained by office jobs, many of which are high-skilled, being overrepresented in the underlying online job postings data (Box 2.4).

However, occupations of all skills groups and in a variety of industries count among the tightest occupations. In Europe, both high-skilled occupations (e.g. Software and Applications Developers and Analysts) and lower-skilled occupations (e.g. manufacturing, transport, and storage labourers) are among the tightest occupations on average across regions. In the United States, the tightest occupations include mostly high-skilled occupations (e.g. management occupations or computer occupations), with few exceptions (e.g. physical therapist assistants). Additionally, jobs in a wide range of industries show high labour market tightness in the United States, namely the ICT, wholesale, arts and healthcare industries, and in Europe, namely the ICT, manufacturing and logistics industries.

Figure 2.7. Labour market tightness is up to four times higher-than-average for the most affected occupations

Labour market tightness by occupation relative to the regional average (=100) for the ten tightest occupations, 2022.

Note: Relative labour market tightness by occupation is calculated at the regional level as the number of vacancies over employment for a given occupation and region, divided by the regional labour market tightness average. The horizontal axis shows the average relative labour market tightness across regions (weighted by employment) in the US and the EU. The figure shows the ten tightest occupations in the US and the EU. Occupations are classified according "minor" occupational SOC codes (4 digit) in the US and 3-digit ISCO codes in the EU.

Source: Own elaboration (see Box 2.3); Lightcast; EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Missing green and digital talent hold back many OECD regions

Tightness estimates at the occupational level allow for aggregations by relevant occupational groups, such as green and ICT jobs, and can provide valuable evidence for policies to target shortages effectively. This chapter uses the task-based definition of green jobs established in previous LEED work, according to which occupations with at least 10% green tasks are classified as green (OECD, 2023[12]). In this definition, tasks that contribute to environmental objectives, such as preserving the environment and reducing greenhouse gas emissions, are considered green. For ICT jobs, this chapter adopts Eurostat’s classification of ICT specialists which 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" (Eurostat, 2024[13]). Since Eurostat defines ICT specialists at the 4-digit ISCO level, this classification is further converted to the 3-digit level based on employment shares and adapted to other occupational taxonomies (e.g. SOC) used in this chapter.

Jobs that are crucial for the digital transition experience high labour shortages in almost all OECD regions. ICT jobs show 156% more vacancies per filled position than the regional labour market on average. Although ICT are tighter than the average job in 97% of OECD regions with available data large regional disparities exist within countries (Figure 2.8). On average, ICT jobs show more than twice (126%) more vacancies per filled position in the region with the highest compared to the one with the lowest ICT shortages.2 This difference can reach up to 700% (Thessaly region in Greece). On average across countries, ICT jobs are tightest in Greece, Portugal and Poland, which have around 4 times more vacancies per filled position in ICT jobs compared to the average job. Nordic European countries, namely Finland, Sweden and Denmark, experience the lowest ICT shortages as ICT jobs are approximately as tight as the regional labour market on average. Apart from job-specific skills, other factors, such as generally higher educational requirements among digital (and green) jobs compared to the average job, likely contribute to these tightness differences.

For green jobs, this gap in relative tightness is smaller than for ICT jobs, but still substantial. Similarly to ICT jobs, green jobs show more vacancies per filled position than the average job in more than nine out of ten OECD regions (91%) (Figure 2.9). With 37% more vacancies per filled position than the average job, the magnitude of this tightness difference for green jobs is substantial, but smaller than for ICT jobs (156%). Regions vary in their tightness of green jobs, as green jobs are 44% tighter in the region with the most relative to those with the least severe green jobs shortages. Yet these regional disparities are smaller than in the case of ICT jobs. Greece experiences the strongest green jobs shortages both at the national level, where green jobs are 95% tighter, and at the regional level, with green jobs in Eastern Macedonia showing more than twice as many vacancies than the average job. The three countries with the lowest green jobs shortages on average are Norway, Sweden and Finland, where green jobs are about as tight as the average job. These Nordic countries also experience relatively little variation across regions, with the exception of Norway where green jobs are almost 57% less tight than the average job in Trøndelag.

Box 2.5. How does labour market tightness affect wages?

Theoretically, occupations and industries that experience labour shortages should see an increase in their real wages. The reason for this is that firms need to improve job conditions (including but not restricted to wages) to attract the relatively few job candidates. Past and current economic projections have repeatedly highlighted that this mechanism could lead to additional upward pressure on wages, potentially exacerbating a wage-price spiral, in the current context of high inflation (OECD, 2024[14]).

In practice, despite widespread labour shortages, real wages have declined across the OECD by 2.2% on average between Q4 2019 and the end of 2022 as nominal wages have not kept pace with inflation in recent years. Although nominal wages grew by 14.3% over the same period, high inflation rates overshadowed the (likely) labour shortages-induced upward pressure on real wages. Real wages performed better (i.e. fell by less) in low- than in medium- or high-paying industries, as a result of stronger nominal wage growth in lower-wage industries (OECD, 2023[3]). This aligns with evidence that labour shortages have increased strongly in lower-paid and lower-quality occupations over this period (Zwysen, 2023[7]).

As inflation fades, labour shortages could lead to higher real wages in affected occupations and industries. Supporting this hypothesis, tentative evidence suggests that industries that faced higher increases in labour market tightness also experienced higher nominal wage growth (OECD, 2023[3]). More specifically, industries that experienced a 1% increase in tightness saw their nominal wages rise by 0.03%. Indeed, evidence from 2023 shows that real wage growth has turned positive in most OECD countries (29 out of 35), standing at 3.5% across the OECD on average (OECD, 2024[2]). However, more detailed regional analysis is not feasible due to the lack of regional and occupational wage data, which represents a major limitation to offer valuable policy options to address the issue of stagnating wages across OECD regions.

Regions with more severe labour shortages for green jobs also experience higher ICT job tightness (and vice versa), highlighting that the digital and green transitions are intertwined. Regions with a 10% higher green jobs tightness level also have a 18% higher tightness of ICT jobs on average (relative to the average regional tightness) (Figure 2.10). This positive relationship likely stems from the fact that the green and the digital transitions – and their associated job profiles – complement each other, as both the innovation and the implementation of green technologies often require an adequate ICT infrastructure and a digitally-skilled workforce. For example, digital technologies, such as “smart” meters can help make industrial processes more energy-efficient, including in the most emissions-intensive industries, such as cement, steel and chemical industries (European Commission, 2022[15]). However, the positive association between tightness of green and digital jobs at the regional level can partly also arise from some occupations being classified as both green and digital.

Figure 2.8. ICT jobs experience particularly severe shortages across OECD regions

Labour market tightness of ICT jobs relative to the regional average (= 100), 2022.

Note: The relative labour market tightness of ICT jobs is based on the relative tightness estimates by occupation, which are aggregated at the regional level using Eurostat’s definition of ICT specialists. Since Eurostat’s ICT specialists classification is defined at a lower occupational level (4 digits) than the available employment data (mostly 3 digits), the classification is aggregated to the 3-digit level assuming equal employment shares among the 4-digit occupations. The vertical axis shows the average relative labour market tightness of ICT jobs relative to the average job in the region’s labour market. Hence, a value above 1 indicates higher-than-average tightness of ICT jobs in that region.

The details for Eurostat’s definition of ICT specialists can be accessed here: https://ec.europa.eu/eurostat/cache/metadata/en/isoc_skslf_esms.htm#:~:text=216)%2C%20Eurostat%20defines%20ICT%20specialists,main%20part%20of%20their%20job%22 (as of 4 July 2024).

Source: Own elaboration (see Box 2.3); Lightcast; EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics; (Eurostat, 2024[13]).

Shortages of workers with green and digital skills are an obstacle to firm investments and inhibit local economic growth (OECD, 2023[12]). In Europe, for example, more than 80% of firms report skills shortages, especially for green and digital skills, which limits the implementation of climate change projects and progress on the green transition (EIB, 2023[16]). This reflects the high demand for workers with green and digital skills as economies adjust to the twin transition and could be a result of a skills mismatch as labour markets undergo a process of structural transformation that is not yet complemented through adaptations in education and training systems.

Figure 2.9. Green jobs are tighter than the average job in the vast majority of OECD regions

Labour market tightness of green jobs relative to the regional average (= 100), 2022.

Note: The relative labour market tightness of green jobs is based on the relative tightness estimates by occupation, which are aggregated at the regional level using the green jobs classification established in (OECD, 2023[12]). The vertical axis shows the average relative labour market tightness of green jobs relative to the average job in the region’s labour market. Hence, a value above 1 indicates higher-than-average tightness of green jobs in that region.

Source: Own elaboration (see Box 2.3); Lightcast; EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics; (OECD, 2023[12]).

Figure 2.10. Shortages in green and ICT jobs tend to co-occur in OECD regions

Labour market tightness of green jobs (horizontal axis) and ICT jobs (vertical axis) relative to the regional average (=100), 2022.

Note: The figure shows the labour market tightness of green jobs (horizontal axis) and ICT jobs (vertical axis) relative to the regional average in 2022 for all OECD regions with available (subnational) data. The dashed line represents the regression line of a linear model with a 95% confidence interval.

Source: Own elaboration (see Box 2.3); Lightcast; EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics; (OECD, 2023[12]); (Eurostat, 2024[13]).

High-productivity and contact-intensive industries experience the strongest labour shortages

Industries differ substantially in terms of labour market tightness, yet the magnitude depends on the geographic area. While jobs in the tightest industry are 2.7 times tighter than the regional average in Australia (mining), the tightest industry in the United Kingdom (real estate activities) stands at 1.8 times the average regional tightness level (Figure 2.11).

High-productivity industries, such as ICT and finance and insurance, tend to be among the tightest industries. Mirroring the ongoing digital transition in many regions, the ICT industry ranks among the five tightest industries in three out of four geographical areas analysed (Figure 2.11). Jobs in the ICT industry display between 1.2 times (Australia) and two times (European regions) more vacancies per filled position than the average job in a region. Moreover, ICT is the tightest industry in 33% of European regions and in 24% of US regions. This aligns with ICT specialist occupations showing particularly high levels of tightness (Figure 2.8). Furthermore, financial and insurance activities and utilities are particularly tight in the United States and Europe. In fact, the finance and insurance industry is the tightest industry in the United States, with 2.5 times more vacancies per filled position than the regional average (1.6 times in the European Union).

Contact-intensive industries, namely health care, and accommodation and food services also show strong signs of labour shortages. In line with reports of widespread shortages of health care workers – a lack of 15 million health workers in 2022 worldwide (Boniol et al., 2022[17]) – the health care industry ranks among the five tightest industries in the United States, the United Kingdom and Australia, with values ranging from 1.4 to 1.6 times the average regional tightness level. Similarly, accommodation and food services are among the five tightest industries in the United Kingdom and Australia (both 1.3 times the regional average), and Europe (1.1 times tighter), reflecting the post-COVID-19 surge in labour shortages in contact-intensive industries (Causa et al., 2022[6]).

Figure 2.11. Industries differ substantially in terms of labour market tightness

Labour market tightness of the five tightest broad industries relative to the regional average (=100), 2022.

Note: Relative labour market tightness by industry is calculated at the regional level as the number of vacancies over employment for a given industry and region, divided by the regional labour market tightness average. The horizontal axis shows the average relative labour market tightness across regions (weighted by employment) in the United States, the United Kingdom, Europe and Australia. Occupations are classified according to broad industries (i.e., the highest level) of the respective industry classification, namely NAICS the US, NACE in the EU, UK SIC in the UK and, ANZSIC in Australia.

Source: Own elaboration (see Box 2.3); Lightcast; EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Labour shortages are larger in regions that rely more on high-growth industries (defined at the national level), highlighting the regional economic structure as a driver of shortages. Regions with a one percentage point higher employment share in high-growth industries show a 2% higher tightness level (relative to the country mean) on average (Figure 2.12). For this analysis, high-growth industries are defined as the three (1-digit) industries with the largest employment growth between 2019 and 2022 at the country level. As demand for workers in high-growth industries is high in many parts of the country, companies in regions with a higher reliance on these industries are likely to face more difficulties in finding adequately skilled employees given increased labour force needs and increased competition from other companies within the region and other regions.

Figure 2.12. Regions that are more reliant on high-growth industries experience stronger shortages

Regional tightness relative to the national level (vertical axis) and the share of employment in high-growth industries, defined at the national level (horizontal axis), 2022.

Note: The figure shows regional tightness relative to the national level on the vertical axis and the share of employment in high-growth industries on the horizontal axis. High-growth industries are defined as the three (1-digit) industries with the largest employment growth between 2019 and 2022 at the country-level. For example, the most common high-growth industries in Europe are “information and communication” in 18, “water supply; sewerage, waste management and remediation activities” in 9 and “real estate activities” in 9 out of 27 countries.

Source: OECD elaboration based on Lightcast, the OECD Region and Cities databases and labour force surveys: EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

The interpretation of tightness estimates for nascent, often high-growth industries requires some caution. By definition, such newly emerging industries are initially characterised by a low number of workers, leading to high levels of tightness compared to other industries (provided that sufficiently many vacancies exist already). Conventional tightness measures can be misleading in this context, since nascent industries recruit from other industries, particularly those that employ workers with a similar skill set. While this caveat is important to keep in mind, it is mitigated in this chapter as the latter examines tightness for approximately 15 broad industry groups per country, within which emerging industries make up a relatively small share.

A look at each region’s tightest industry in Europe and the United Kingdom reveals a mixed pattern (Figure 2.13). In some countries, most regions experience the strongest shortages in finance and insurance (e.g. France, Sweden, and the Czech Republic), while in others, most regions’ tightest labour markets are ICT (e.g. Spain, Portugal, Switzerland, and Poland) or utilities (e.g. Germany). Italy presents a mixed picture, as regions vary in terms of their tightest industry (utilities, ICT, and manufacturing).

Figure 2.13. ICT and utilities are the tightest industries in more than half of all European regions

Tightest industry in each region, 2022.

Note: The figure shows each region’s tightest industry in 2022. Industries whose employment is below 5% of a region’s median are excluded.

Source: Own elaboration (see Box 2.3); Lightcast; EU-LFS, UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Demographic change will put additional pressure on labour market tightness

More than four in ten OECD regions saw their potential labour force shrink over the past decade (Figure 2.14). This proportion increased from almost 30% in 2008 to over 40% in 2022 (OECD, 2020[18]). In almost 10% of regions the working age population (15- to 64-year-olds) shrank by more than 10%, in many cases as a result of both ageing and outmigration. On average, OECD regions experienced modest growth in their potential labour force of about 3%. Notably, more than a quarter of regions grew their potential labour force by more than 10%. Thus, the varied trends in the evolution of the working age population across regions suggests the need for tailored support for those regions facing a significant contraction, especially as it potentially aggravates labour market tightness.

Figure 2.14. Over 40% of OECD regions experienced a decline in the working age population over the past decade

Note: The figure shows the share of regions in each country which belong to each of the four categories representing the percentage change in the working age population in 2012 and the working age population in 2022. The sample of countries includes the OECD accession countries of Bulgaria, Croatia and Romania.

Source: OECD calculations based on OECD Regional Labour Database.

By 2042, the median local labour market tightness across OECD regions is projected to increase by almost 9%, solely due to the decrease in the working age population in ageing regions (Figure 2.15). Most OECD countries are ageing resulting in shrinking working age populations (OECD, 2022[19]; OECD, 2023[20]). These ageing populations will further exacerbate local labour market tightness, especially in the places that face the greatest demographic change. Using information on regional population structures, this chapter calculates the projected percent change in labour market tightness that can be directly attributed to regional demographic shifts. Information on the methodology behind the calculation is detailed in Box 2.7.

Box 2.6. Lower labour force participation and employment rates among older workers intensify labour shortages

A pandemic-induced increase in labour market attrition (i.e. labour force exits) among older workers intensifies demographic pressure and contributes to shortages in the US labour market. Older workers’ (aged 55-70) likelihood of retiring during the first year of the COVID-19 pandemic rose by 6.7 percentage points (43%). This effect was particularly strong for older workers without a college degree and those in contact-intensive jobs (Davis et al., 2023[21]), suggesting that health concerns played a role in these retirement decisions. Furthermore, labour force participation rates among workers aged 55-74 have not recovered to pre-pandemic levels and remained below 40% at the end of 2022 (Botelho and Weißler, 2022[22]). At this point it remains unclear to what extent older workers who left the labour force (mainly to retire) during COVID-19 in the United States will return to the labour market in the long run.

In contrast, the European Union only experienced a temporary drop in labour force participation rates of older workers during the COVID-19 pandemic. In Euro area countries, labour force participation quickly returned to its pre-COVID growth path, which is largely in line with ageing population trends, after the pandemic. As a result, in 2022 more (41.5%) workers aged 55-67 than before the pandemic (40%) were working or actively looking for employment (Botelho and Weißler, 2022[22]). However, employment rates show a different pattern: about the same share (95%) of older workers in the labour force (aged 60-64) in the European Union are employed in 2022 compared to 2018, while employment rates increased for all other age groups over the same period. This stagnation suggests that older workers in the European Union face difficulties in finding employment even if they are willing to participate in the labour market. These difficulties may include negative perceptions of older workers by employers and a lack of skills in an increasingly digital job market (see below).

In the regions with the oldest population structure, demographic change will increase labour market tightness by 2042 almost twice as much as in the OECD median region. The difference between older regions, defined as those currently in the top 20% of regions with the highest old-age dependency ratio (i.e. the ratio of the population aged 65 and over to the working age population), and the OECD regional median will be increasing over time. In contrast, for younger regions in the bottom 20% of the regional distribution of old-age dependency ratios, labour market tightness will only marginally decrease by about 2% by 2027 and remain relatively stable up to 2042. Even in regions where the working-age population is expected to grow, the easing of labour market tightness is limited, especially compared to the impact of ageing populations.

Figure 2.15. Demographic pressure will tighten labour markets, especially in older regions

Projected labour market tightness given net working age population change, 2022 to 2042.

Note: The figure shows the projected increase in labour market tightness over the years 2022 to 2042, where 2022 is indexed to 100. The figure shows the evolution of absolute tightness for regions in the top 20%, bottom 20% and the regional median of the old-age dependency ratio. The group of regions in the top and bottom 20% each account for at least 20% of the population in the region.

Source: OECD elaboration based on Lightcast, the OECD Region and Cities databases and labour force surveys: EU-LFS (including the OECD accession countries of Bulgaria, Croatia and Romania), UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Box 2.7.  Calculating demographic pressure on labour market tightness

“Back-of-the-envelope” exercise

Regions are ageing across the OECD (OECD, 2022[19]; OECD, 2023[20]). This demographic change is likely to have diverse implications for local economies and local labour markets in particular. In the case of labour market tightness, shifts in the population structure are especially pertinent since the degree of tightness is directly related to the available supply of potential workers, i.e. the working age population.

To gain insight into the expected impact of population change within regions on the intensity of labour market tightness, this note uses a simple exercise. In short, the exercise seeks to answer the question: By how much would labour market tightness increase given the projected net change in the working age population?

Methodology

The first step calculates the projected working age population for five-year periods from 2022 to 2042 (i.e. up to 20 years from the last available year, 2022). The projected working age population takes into account the hypothetical net change in the working age population, defined as 15- to 64-year-olds, given current demographic age structures. For example, in the year 2027, the projected working age population is calculated as the working age population in 2022 minus those individuals expected to retire by 2027 (i.e. those aged 60 and over in 2022), plus those individuals expected to join the working age population by 2027 (i.e. those aged 10 and over in 2022). The projection for 2042 assumes that births in the next five years will be the same as the currently youngest 0- to 5-year-old cohort. Information on the demographic structure of regions is taken from the OECD Regions and Cities databases (OECD, 2024[23]).

Next, the projected working age population is used to calculate an alternative measure of labour market tightness. The main measure of labour market tightness defines tightness as the number of vacancies over the number of employed persons (see Box 2.3). For the purpose of this exercise, labour market tightness is instead defined as the number of vacancies over the working age population (15- to 64-years-old).

This change in methodology avoids additional assumptions on employment rates and their evolution across demographic groups. For example, using the projected number of employed persons would either require knowing the employment rate of young people and of older workers over the next twenty years, or assuming that the rate is constant over this period and for each group. Both of these scenarios seem unlikely, as employment rates differ greatly across age groups and over time.

Furthermore, as the interest of this exercise is to calculate the projected percent change in tightness given a demographic change, the most important aspect is the comparability of the measure to 2022, the last year with available data. By redefining the measure, comparability is maintained with minimal confounding assumptions. Additional assumptions, which are important to keep in mind, are that the regional fertility in the next five years is the same as the past five years, that no fatalities occur over the next two decades, and that net migration in the region is constant.

Lastly, to calculate the percent change in labour market tightness given the projected working age population, each five-year period is indexed to 2022, the last available year. In this way, the exercise describes the change in labour market tightness attributed only to the predicted change in the working age population.

This implies that the exercise does not take into account how the change in the population structure could affect the number of vacancies. Vacancies are taken as the value in 2022, which is kept constant. It could be that a fall in the labour force affects the number of vacancies. This correlation could be either positive or negative: vacancies could rise due to increased demand for labour-intensive care work, or they could fall since older populations tend to demand less goods and services. The net effect of these two dynamics could also be country or region-specific and so, different from the average net correlation. The exercise keeps vacancies constant in order to clearly attribute the change in tightness solely due to the change in the labour force.

Notes: Job vacancies are provided by Lightcast; population data comes from the OECD Regions and Cities database (OECD, 2024[23]).

Figure 2.16. Ageing populations will affect almost all OECD countries, albeit to varying degrees

Average projected change in labour market tightness given net working age population change by country, indexed to 2022.

Note: The figure shows the mean of the projected increase in labour market tightness for 2032 and 2042, relative to 2022 (indexed to 0), for OECD countries with available data.

Source: OECD elaboration based on Lightcast, the OECD Region and Cities databases and labour force surveys: EU-LFS (including the OECD accession countries of Bulgaria, Croatia and Romania), UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Most OECD countries will be affected by demographic pressure on labour market tightness. By 2042, in 25 out of the 26 countries for which data is available, the national mean of regional labour market tightness is expected to increase due to demographic pressure (Figure 2.16). Yet, the degree of this increase varies greatly across countries. The highest increase is projected to be in Italy, where labour market tightness is projected to increase by almost 30%, followed by Portugal (24.2%), Spain (22.9%), and Greece (21.7%). In contrast, Ireland is the only country where demographic change will decrease labour market tightness in 2042, but the degree is negligible (-0.9%).

Over the next 10 and 20 years, demographic change is projected to lead to increased labour market tightness in a majority of OECD regions. Labour market tightness will increase by over 5% in 38% of OECD regions by 2032, and 72% of OECD regions by 2042 (Figure 2.17). Asturias (Spain), Sardinia (Italy), and Liguria (Italy) are the most affected regions, where labour market tightness in 2042 will be more than 40% higher relative to 2022.

Some regions will experience a notable easing of labour market tightness due solely to demographic change. For example, in Utah (United States), labour market tightness is projected to decrease by nearly 14% by 2042, followed by North Dakota (United States) and the Northern Territory (Australia) with a projected decrease of almost 7% in each. This finding contrasts with the aggregate results at the OECD and country levels, which found minimal projected decreases in labour market tightness. Thus, it highlights the diversity of regional demographic structures and challenges.

Figure 2.17. Most OECD regions will experience increases in tightness due to demographic change

Projected change in labour market tightness given net working age population change for 2032 (top) and 2042 (bottom), indexed to 2022.

Note: The figure shows the share of regions where the projected change in absolute labour market tightness is over 5%, under 5%, under -5% and over -5%, for OECD countries with available data. The projected change is shown for 2032 (top) and 2042 (bottom), relative to 2022.

Source: OECD elaboration based on Lightcast, the OECD Region and Cities databases and labour force surveys: EU- LFS (including the OECD accession countries of Bulgaria, Croatia and Romania), UK-LFS, Bureau of Labour Statistics (USA), and Australian Bureau of Statistics.

Potential policy levers to alleviate labour shortages

Local and national governments can turn to multiple policy levers to mitigate local labour shortages. This section gives an overview of the key policy areas, namely labour-saving technologies, supporting the economically inactive, labour market intelligence tools, up- and reskilling, and (im)migration. For each of these policy areas, this section provides tangible policy examples that contribute to alleviating labour shortages in OECD regions.

Leveraging labour-saving technologies

In the context of addressing labour shortages, technology presents an opportunity to increase productivity, enhance job quality, and attract workers to jobs previously considered less desirable – while also recognising the need to mitigate the potential risks of technological change. From Plato to Keynes, concerns about technology and automation’s disastrous impact on work and society are often expressed (Frank et al., 2019[24]; Keynes, 1930[25]). While historical fears of a worker-less future have failed to manifest, evidence does show adverse effects of technological change on some workers. For example, workers who lose their jobs in mass layoffs — including in sectors subject to significant automation — often never recover their previous wage, and some workers and places may be left behind, even as there is net job growth (Vermeulen and Braakmann, 2023[26]; Autor and Dorn, 2013[27]; Acemoglu and Restrepo, 2019[28]). With regard to artificial intelligence, for example, Generative AI such as Chat-GPT, its impact on work is difficult to measure. So far, the evidence suggests that the impact of AI on jobs is limited to large firms experimenting with the technology. Where firms have adopted these technologies, they are reluctant to fire workers as a first step, opting instead to adjust hiring practices (OECD, 2023[3]; Acemoglu et al., 2022[29]). In the future, the potential effects remain to be seen but are likely to differ from these early effects, as the technology will progress and expose a much larger and different pool of workers to its impact (Eloundou et al., 2024[30]).

The use of AI can help alleviate pressures in industries from healthcare to manufacturing that are suffering from labour shortages (see Chapter 3). In the healthcare sector, AI technologies focused on streamlining hospital operations, diagnostic assistance, and nursing practices can free up time for patient care. In regions experiencing labour shortages in manufacturing, robotic automation can reduce operating costs significantly, ensure a more optimised production, help avoid injuries and human error, and increase the quality of output and reduce product defects. Regions with a strong presence of agriculture could also benefit from AI through precision farming technologies, automated irrigation and pest control, enhancing crop yields and reducing the need for some types of human skills. Box 2.8 gives examples of industries in which AI are already used to mitigate labour shortages.

Box 2.8. AI to the rescue: how automation alleviates local labour shortages in manufacturing and agriculture

Significant labour shortages are evident in both manufacturing and agriculture in countries such as the United States, jeopardising economic stability, growth and productivity. However, AI can offer a number of solutions to alleviate labour shortages. For example, robotic automation can enhance efficiency by handling repetitive tasks in manufacturing, while precision farming and drones can help to maintain crop yields in agriculture and thereby profitability. Consequently, funding for research and development of AI and automation in these industries can help address the challenges posed by persistent labour shortages.

Manufacturing

In manufacturing, AI and robotic automation can enable continuous production, with safer and more efficient operations, help with heavy material handling and repetitive movements, and facilitate quality checks (Forbes, 2022[31]; World Economic Forum, 2024[32]). Tool Gauge, an aerospace component manufacturing company based in Tacoma, Washington, turned to automation to address its labour shortage when the company needed to hire around 100 new employees who were not available in the local labour market (International Society of Automation, 2020[33]). As a solution, Tool Gauge instigated the use of collaborative robots (cobots) to operate repetitive and high-labour applications in its metal and plastic parts areas. The use of cobots reduced labour needs by 75%, as it only requires one operator to work with the cobot to produce four hundred units per day (International Society of Automation, 2020[33]).

Agriculture

In the US agriculture industry, labour shortages pose an ongoing threat to the industry’s profitability and crop yields (BBC, 2024[34]). AI can be one way to mitigate these issues. A recent estimate puts the economic value of AI to US agriculture – through labour and input cost savings, and increased crop yields – at USD 100 billion (McKinsey, 2024[35]). Indeed, some estimates show an increase in AI adoption among US agricultural businesses – from 54% in 2018 to 87% in 2022 (RELX, 2021[36]). To unlock these potentially large economic benefits, the Japanese government promotes “smart agriculture” through its “Smart Agriculture Demonstration Project”, specifically to mitigate current and future labour shortages in the industry. The project is aimed at automising operations, for example through the use of robot tractors and water management systems that can be operated via smartphones, thereby reducing the demand for on-farm workers. Additionally, AI-based analysis of remotely sensed data and weather data taken by drones and satellites improves the accuracy and efficiency of many aspects of farming, including disease control, water management, and crop prediction. These technologies are often known as “precision farming” and further reduce the need for on-farm workers (Ryan, 2023[37]) (Japanese Ministry of Agriculture, 2023[38]).

By reducing the need to do repetitive tasks, the use of automation and AI allows workers undertake more valued work, addressing one of the main drivers of the post-COVID-19 rise in labour shortages: low-paid and low-quality jobs. Studies report that some of the largest recent increase in labour shortages is observed in industries with difficult work environments (such as Food and Services, Manufacturing, and Retail Trade), noting that a rise in quit rates accompanies the rise in labour shortages (Duval et al., 2022[39]; Pizzinelli and Shibata, 2023[40]; Causa et al., 2022[6]; Zwysen, 2023[7]). At the same time, in firms that are early adopters of the technology, workers report a positive impact on job quality (OECD, 2023[3]). However, this is likely to be limited to jobs that do not require constant face-to-face human interactions, excluding some service sector jobs in hospitality, catering, and retail sales, for example. Where job responsibilities permit, improving job quality through AI could alleviate a contributing factor to labour market shortages, although special attention must be given to support workers whose jobs are potentially displaced.

Bringing the economically inactive (back) to the labour market

Local economies can also harness untapped potential in their population by removing barriers to work for specific groups with low labour force participation. This section focuses on mothers, youths and elderly people, discussing the reasons for their often-low economic activity as well as policies that facilitate their return to the labour market.

Women, and in particular mothers, are less likely to work than men of similar age, which is highlighted by a 15 percentage points lower employment rate among 15-65 year old women (62%) than among men (77%) in 2022. One reason for this is that mothers often engage more in child care than fathers, impeding them from taking up paid work. Regional governments can facilitate the return to work for mothers, by improving the accessibility, affordability, and quality of their early childcare systems (European Commission, 2023[41]). A study of the 1990s reforms in Spain that led to an expansion of subsidised childcare for 3-year-olds finds an increase in maternal employment of 9.6% (Nollenberger and Rodríguez-Planas, 2015[42]). The impact of region-based policy is further evidenced in recent years, as the federal government of Canada implemented a “CAD-10-a-day” childcare framework through agreements with all provinces and territories, with adaptations between them (Box 2.9). This policy was developed with the aim to bring more women into the labour force. Since its implementation, the labour force participation among working age mothers with young children has reached a record high of 79.7%. The government is now making further progress by increasing inclusive access to child care, with funding focused on underserved communities (Employment and Social Development Canada, 2024[43]).

Box 2.9. Unlocking women’s labour force participation: The case of the CAD 10 a day child care programme

In 2021, the Government of Canada announced CAD 30 billion investments towards affordable child care, with one of the goals being increasing women’s labour force participation. The federal government negotiated Canada-wide Early Learning and Child Care (ELCC) Agreements with each province and territory to address their specific needs. These investments also include funding for Indigenous early learning and childcare through partnerships tables.

• Nunavut, a territory with the smallest population in the country, was the first jurisdiction to reduce fees to a flat rate of CAD 10-a-day under the federal programme, which resulted in an 82% reduction in parent fees in Iqaluit, on average, – the largest fee reduction of any city in Canada in 2023. Families in Nunavut are now saving up to CAD 14 300 per year, per child.

• In Fall 2024, Newfoundland and Labrador started a new non-standard hours of care trial to provide family child care services funding for up to 13 hours of extended daytime child care or up to 13 hours of overnight child care.

• In Manitoba, the Ready-to-Move (RTM) Child Care project offers a collaborative approach to construct a large number of child care facilities servicing infant and pre-school spaces within the province under expedited timelines and in areas with a demonstrated child care need. Under this initiative, RTM construction costs will be fully funded under the Canada-Manitoba Canada-wide ELCC Agreement in exchange for land, servicing and free rent for the child care operator.

• Ontario, one of the most populous and urbanised provinces, has enrolled 92% of licensed child care providers in the Canada-wide ELCC program for children aged 0-5 as of June 2024. The province has implemented a phased approach to reducing parent fees in licensed child care. In 2022, Ontario introduced a fee reduction of up to 25% retroactively by April, reaching a 50% reduction by the end of the year. Moving forward, Ontario plans to introduce a parent fee cap of CAD 22 per day for eligible children starting January 1, 2025, with the goal of achieving an average of CAD 10 per day by 2026.

Preliminary results suggest that these investments in ELCC and jurisdictional adaptations have likely increased female labour force participation. The Bank of Canada noted that “the rise in the participation rate of women could be due to lower average fees for child care since April 2022”. The Statistics Canada Labour Force Survey continues to show positive trends, including a 79.7% labour force participation rate among mothers aged 25-54 with young children in 2023, up from 75.9% in 2019.

Source: (Employment and Social Development Canada, 2022[44]) (Government of Newfoundland and Labrador, 2024[45]) (Government of Manitoba, 2023[46]) (Government of Ontario, 2022[47]) (Employment and Social Development Canada, 2024[43]) (Statistics Canada, 2024[48])

For young economically inactive persons, support programmes can facilitate their labour market participation. Across OECD countries, 12.6% of young people (15-29 years) were neither in employment, education, nor training (NEET) in 2022. To reduce the NEET rate, governments in OECD countries run various programmes that equips youths with job experience and new skills. For example, the Seoul Metropolitan Government (SMG) in South Korea, under its City of Global Professionals framework, has made use of several mechanisms to enhance youth employment. These mechanisms include free training on software development with the aim to reduce job mismatches, and an internship camp to provide on-the-job experience. In this way, the SMG facilitates the entry of unemployed youths into SMEs, as it actively connects them with employers, provides professional work experience, and skills training (Seoul Metropolitan Government, 2024[49]). Additionally, Mexico implemented the programme Youths Building the Future which provides financial support for apprenticeships, training, and social security for young people (aged 18-29). The programme has to date benefited almost 3 million youths who are on average 2.7 times more likely to find employment than those who did not participate (Secretaria del Trabajo y Prevision Social, 2024[50]) (Comision Nacional de los Salarios Minimos and Secretaria del Trabajo y Prevision Social, 2023[51]).

Decentralising the higher education system by opening new higher education institutions (HEIs) in non-metropolitan regions can have the potential to increase place attractiveness — in particular for young workers and firms. Many youths and young workers in OECD countries migrate from remote to metropolitan regions, in which educational (and professional) opportunities are often concentrated (OECD, forthcoming[52]). To counter the out-migration of young people from remote regions, which exacerbates labour shortages in regions in demographic decline, and to support the latter’s regional economic development, some OECD countries decentralize the education system by strategically placing new Higher Education Institutions (HEIs) away from metropolitan areas. By opening new HEIs in less developed regions, governments – apart from attracting students – also aim at attracting innovative companies which can recruit directly among the new HEIs’ pool of highly skilled graduates. This can in turn create new job opportunities for workers in these regions.

To enable HEIs in remote regions to create economic opportunities for workers and firms, their educational programme needs to reflect the local skills demand. Innovation spillovers between universities and firms can foster employment and wage growth at the local level (Hausman, 2022[53]). Yet, bringing these positive dynamics to remote regions, for example by strategically opening new HEIs, remains challenging. Openings of new HEIs in Italy from 1985 - 2000 and Switzerland between 1997 and 2003 increased firms’ innovation activity (i.e., measured as patents) by 7% per university (Cowan and Zinovyeva, 2013[54]) (Pfister et al., 2021[55]). In Switzerland, especially small and medium-sized enterprises in regions outside major innovation hubs benefitted. However, a similar policy in Germany in the 1980s and 1990s shows that newly opened HEIs may mostly benefit already dynamic labour markets (Berlingieri, Gathmann and Quinckhardt, 2022[56]). For HEIs to contribute to sustained economic development in remote areas, for example by attracting workers and firms, the HEIs’ educational content needs to be adapted the needs of local businesses as Närpes, a small locality in Finland, shows (Box 2.10).

Box 2.10. Answering the needs of business and the local community: The case of vocational schools in Närpes, Finland

Närpes, a small locality in Western Finland, provides and adapts vocational education that suits the needs of the local economy, which has been part of the success of its regional attractiveness. This is achieved as the region closely collaborates with the main businesses, resulting in a curriculum that includes logistics, metalwork, constructions, and home care, reflecting the needs of the locality. The educational curriculum is repeatedly adjusted, as evidenced in a recent pilot initiated with the purpose of satisfying the high demand of welders from the Närpes Trä & Metall (NTM) company. This pilot study was the result of close collaboration between the school, Närpes municipality and government which provided funding and the Regional Centre for Economic Development, Transport and the Environment (ELY-Centre) which developed the pilot. The pilot offered a specialization in wielding techniques and was designed for 30 people which were filled immediately.

Despite its rather remote location, Närpes has had a positive population inflow over the last decade, and a 10% higher employment rate in comparison to the national average, partly attributed to its vocational school approach that answers the needs of business, good immigration policies, and an efficient healthcare system. The number of people in adult education has been growing, and most students find a job in the region upon graduating. The customized education system is part of the reason that attracts new workers internally and internationally, as well as retaining local workers by offering ways to switch into other professions.

Source: (Michael Kull, 2019[57])

Older workers also show low labour force participation rates due to the challenges they face in the labour market. This is evidenced by the fact that, in 2022, only 67.4% of people aged 55-64 participated in the labour force, compared to 86.4% of adults aged 25-54. Some older workers may struggle to keep up with technological advancements, such as the ongoing digitalisation of many job tasks, as they are less likely than younger workers to participate in job-related training. Additionally, employers often perceive older workers as “too expensive” due to tenure-based pay progression, which is a common human resources practice (EBRD, 2020[58]) These factors make older workers particularly vulnerable to lay-offs in work force restructuring (ILO and IFC, 2021[59]), after which they struggle to be reemployed. Japan addresses some of these issues through its Act on Employment Security of Elderly Persons (AESEP) (Box 2.11).

Box 2.11. Japan’s Act on Stabilization of Employment of Elderly Persons

For activating the older population, Japan’s Act on Employment Security of Elderly Persons (AESEP) promotes employment security of older individuals. AESEP prohibits employers from imposing mandatory retirement under 60 and requires them to take measures that aim at providing job opportunities for elderly persons up to 70, including continuous employment. Continuous employment involves allowing workers to retire at the standard retirement age but then rehiring them or extending their employment under new terms, including adjusted wages. Unlike raising or abolishing the mandatory retirement age, which continues employment under the same conditions, continuous employment typically involves a formal retirement followed by re-contracting workers. Wages may decrease under the new contract depending on the job duties after the re-contracting

Data from 2023 show that 3.9% of firms with 21 or more employees have abolished the mandatory retirement age, 26.9% have raised the mandatory retirement age, and 69.2% introduced a continuous employment system as measures to secure employment for elderly persons up to 65. As efforts to secure job opportunities for elderly persons up to 70, 3.9% abolished the mandatory retirement age, 2.3% have raised the mandatory retirement age, and 23.5% introduced a continuous employment system. The Ministry of Health, Labour and Welfare also introduced a wage subsidy of up to YEN 500 000 (roughly USD 3 200 in 2024) to employers who improve their employment management systems for older workers and up to YEN 300 000 (roughly USD 2 000 in 2024) for raising the retirement age to 65.

Source: (Kajitani, 2023[60]; Ministry of Health, Labour and Welfare of Japan, 2023[61])

The barriers to work for older people also include age-based discrimination (e.g. in the hiring process), leading to difficulties finding and applying for jobs. To combat age-based discrimination in the hiring process, France’s Public Employment service is now using aptitude tests to select candidates for interviews and help overcome the age bias in hiring (OECD, 2019[62]). In an alternative approach, the Government of Canada promotes awareness initiatives to promote labour force participation of older individuals by informing employers, unions, and general society about the advantages of recruiting and retaining older workers. The federal government funded various awareness projects in provinces, predominantly in Quebec and Prince Edward Island, while other provinces such as Alberta, Nova Scotia, and British Columbia have implemented their own awareness action plans (Employment and Social Development Canada, 2018[63]).

Using effective labour market intelligence tools

Labour market intelligence tools can help identify labour shortages and facilitate better matching between local vacant positions and available workers, including among the unemployed. Knowledge about current and future trends in the labour market is crucial to anticipate which occupations and industries are vulnerable to labour shortages. For example, CEDEFOP’s European Skills Forecasting model provides information about labour market trends for occupations and industries. By 2035, it estimates that France will need to fill roughly 880 000 positions for science and engineering professionals, consisting of 305 000 new positions and 575 000 workers who will need to be replaced (Cedefop, 2023[64]).

Novel approaches produce timely estimates on regional occupation and skills demand, going beyond more aggregate information based on employer surveys that usually come with a larger time lag. While many statistical offices provide estimates of job vacancy rates and the extent of labour shortages at a rather aggregate level, for example at the industry-level or by firm size, and sometimes for an entire country, advances in data availability (e.g. from online job postings) and text analysis (i.e. natural language processing) have given rise to more fine-grained insights. NESTA, the United Kingdom’s innovation agency for social good, provide demand estimates for roughly 100 occupations and their required skills in 228 UK Travel-to-Work Areas using online job vacancy data. Similarly, Skills Future Singapore, a government agency, created a Jobs Skills Dashboard with which users can see the most common hiring companies and industries for a given job title and skill (SkillsFuture Singapore, 2023[65]). Using online vacancy data, Skills Future Singapore also identifies priority skills for specific industries, such as the green, digital and care economies (SkillsFuture Singapore, 2024[66]). The Austrian PES launched a large language model-based AI tool, called Jobinformat (Job Informant), that provides jobseekers with information on job profiles and related training paths (box 1.20 in chapter 3). Box 2.12 gives additional examples of innovative approaches used by PESs in OECD countries.

Box 2.12. PES increasingly use labour market intelligence tools to facilitate job matching

New types of labour market data, such as job vacancies, along with advances in statistical and artificial intelligence (AI) methods, allow PES in many OECD countries to better support job seekers and employers. More precisely, through the use of novel data sources and AI, PES can support a larger number of clients in a more personalised manner and with more up-to-date insights.

As of 2024, half of OECD PES augment their services with some form of AI technologies. Yet, the use cases and tools substantially differ between PES. Matching systems that recommend suitable job opportunities to job seekers, which are currently used by 20% of OECD PES, are the most common application. These systems are increasingly competency-based through the use of occupation and skills taxonomies. Other use cases include the provision of information (e.g. on available services, measures and benefits), jobseeker profiling tools to better understand their needs (both 17% of PES), and tools to guide career management and job-search strategies of jobseekers (15% of PES). In recent years, PES also started supporting employers in creating vacancy postings (20% of PES), for example by drafting vacancy descriptions and correctly classifying occupations using AI technologies.

The PES in Flanders, Belgium (VDAB), uses the Jobbereik (Job Reach) tool which provides jobseekers with occupations that jobseekers could pursue based on their current role’s competencies and transferable skills. Based on data from job vacancies and deep learning, this tool supports job mobility and transitions. VDAB currently also develops a new functionality which identifies a jobseeker’s skills gap with alternative career paths and suggests training courses to close the gap. Additionally, Competentiecheck (Competency check) allows jobseekers to assess whether their skills are up-to-date by letting them evaluate their level of familiarity with their occupation’s most important competencies. This tool is also designed to provide training and job suggestions for the user.

France Travail, the French PES, uses two versions of an AI-powered tool to help job counsellors and job seekers navigate available active labour market policies (ALMP) if a job seeker is unlikely to find a job quickly. Since 2017, job counsellors can use the Mon Assistant Personnel (MAP – My Personal Assistant) tool to obtain individual-specific recommendations for ALMP support and job opportunities, based on a jobseeker’s CV. This tool, based on various types of AI (reinforcement learning, machine learning and an expert model), assists job counsellors and allows them to spend time on other important tasks rather than replacing them. Similarly, jobseekers can access their version of this tool (Personalised Recommendations) in France Travail’s online portal to obtain recommendations for services and measures – including suitable training opportunities and available workshops (e.g. to improve their CV and/or interview skills).

Source: (Brioscú et al., 2024[67])

In combination with job-level information on skills requirements, AI and traditional statistical tools can also steer job seekers towards future career paths that match their skill set. Using online job vacancy and regional employment data from Italy, the United Kingdom and the United States, (Basauri, Kleine-Rueschkamp and Vermeulen, 2025 (forthcoming)[68]) identify feasible career transitions based on a sufficiently high skills overlap between the old and the new job, and the regional availability of the new job as most workers look for jobs within their region. This framework can guide career transitions from polluting to neutral and green jobs. The analysis also shows that workers in polluting jobs share between 57% and 86% of skills (depending on the country) with the five closest neutral jobs, on average.

Updating the skills provision to reflect local needs

Labour shortages are also the result of a gap between the skills required by employers and those available in the workforce, particularly in the context of growing demand for certain skills arising from megatrends like the green and digital transitions. Vocational education and training (VET) and adult education can mitigate labour and skills shortages by equipping the workforce with sought-after skills.

Vocational education and training (VET) can provide students with necessary technical skills and practical experience at the start of their career. VET offers the potential to prepare students for technical professions, many of which experience shortages, while also facilitating the school-to-work transitions and offering a pathway to higher education (OECD, 2023[69]; European Commission, 2023[1]). For example, 90% of VET students in Germany pursue studies in the dual system in which trainees split their time between studying at a vocational school and working at a company, which usually takes around three years to complete (OECD, 2022[70]). This dual system provides trainees with practical experience and broadens their employment opportunities (OECD, 2022[70]).

Governments are increasing the attractiveness of VET education, to reduce the shortage of skilled workers. For example, Germany anticipates a shortage of 240 000 skilled workers in 2026, yet the VET system experiences high dropout rates of apprentices (26.7% in 2019) and a significant decline in VET training contract numbers (Affairs, 2022[71]; Cedefop, 2023[72]). In 2022, the German government introduced the Excellence for VET initiative that addresses these issues through financial support for high-performing trainees, improved vocational guidance (including digital formats), a push for international education of trainees, and overall greater promotion of VET programmes (Affairs, 2022[71]). In 2024, Austria introduced more advanced vocational training cycle (i.e. at the fifth out of eight levels of the European Qualifications Framework) to offer further specialization for skilled workers, namely those who have already completed initial vocational training or have sufficient job experience (Austrian Ministry of Labour and Economy, 2024[73]). Educational and industry experts as well as social partners are currently designing the new degrees such that they target the needs of the economy, including green and digital skills.

Governments should adapt their skills and training systems to reflect shortages of skilled workers at the regional level. For example, Australia has acknowledged VET as key to tackling regional and rural skills shortages, particularly for nurses, teachers, and engineers (Australian Ministry of Employment and Workforce Relations, 2023[74]). Since 2018 the French national government establishes regional agreements, the so called Pactes Régionaux d’Investissements dans les Compétences (Regional Agreements for Investments in Skills), to improve the regional offer of training programmes. The programmes are designed to equip two million low-skilled jobseekers and youths distant from the labour market with the skills for regional shortage occupations until 2022 (Box 2.13).

Box 2.13. France adapts its large-scale national skills agenda to regional needs

The French government supports low-skilled youths and the long-term unemployed through the Plan d’Investissements dans les Compétences (PIC), introduced in 2018. This policy promotes the skills development of these target groups by financing both already existing and new training programmes and is embedded in France’s strategy to equip the workforce with the skills for the digital and green transition. To provide training (and other support services) to the 2 million planned participants between 2018 - 2022, PIC received 15 billion Euros in funding (French Ministry of Labour, 2018[75]).

Regional agreements, the Pactes Régionaux d'Investissements dans les Compétences (PRIC), receive half of the funding and ensure that the training programmes address the regions' needs. Regions and the national government define the content and the financing of the educational offer in the PRICs. To do so, regional actors determine the priorities of the training programmes, both in terms of content and target group, based on available labour market data on employment, (anticipated) recruitment difficulties, and businesses’ skills needs. As a result, training programmes target regional shortage occupations (France Stratégie, 2018[76]). Regions, which commit to spending at least the same amount as before the introduction of the PIC in 2017, and the national government share the costs of the training programmes (French Ministry of Labour, 2018[75]). Regions and the national government currently negotiate new PRICs for the period 2024-2027.

The PRICs improve the training offer at the regional level via increased capacity of training programmes and additional measures, such as lowering financial barriers. The Occitanie region planned to train 20 000 additional youths and job seekers, elevating the number of trained job seekers to 80 000. Additionally, the regional agreement includes remote learning options, the on-the-job training, information activities in rural urban priority areas, and a labour market observatory to better understand the companies’ skills needs. The regional and the national governments share the associated costs, financing 877 million EUR and 569 million EUR, respectively (Occitanie Region, 2024[77]). To lower financial barriers, the majority of regions has put into place financial aid for commuters and child care. The Bretagne, Normandie, and Pays de la Loire regions have also raised the pay of apprentices (Dares, 2024[78]).

A scientific committee evaluates the training programmes on a regular basis. In their third report, the committee finds that, relative to 2017, regions that signed a PRIC showed a 48% (+0.73 billion EUR) increase in training-related expenditure per year on average, and a 44% (+169 000) increase in the number of participants in 2021. However, regions differ in the effectiveness of their regional agreements as some regions struggle with a lack of information among potential trainees, competition with other training programmes, training providers selecting among interested individuals, and a lack of attractiveness of additional training in the context of a tight labour market (Dares, 2022[79]).

Note: An interactive map shows examples of PRIC initiatives around France: https://travail-emploi.gouv.fr/carte-decouvrir-les-initiatives-en-region-du-plan-dinvestissement-dans-les-competences (accessed on 14 October 2024).

Similarly, the curricula of VET programmes need to be updated to reflect emerging skills demands for green and digital jobs, thereby mitigating current and future shortages. As part of the European Green New Deal, many European countries are introducing programmes that aim at redesigning their VET systems such that they teach the skills required for digital and green jobs (Cedefop, 2024[80]). For example, Estonia started updating its VET curricula based on labour market monitoring and future skills forecasts (Cedefop and Refernet, 2023[81]). Furthermore, Austria has adapted the educational content of more than eighty apprenticeship programmes to the needs of the green and digital transitions in collaboration with companies and social partners. To this end, it also created new apprenticeship programmes, such as Climate-Oriented and Urban Gardening (“Klimagärtner/-in”) or Community Heating (“Fernwärmetechnik”) in 2024 (Austrian Ministry of Labour and Economy, 2024[82]). Box 2.14 gives further examples of initiatives that redesign VET systems to tackle skills shortages.

Box 2.14.  Skills for Success: Modernising VET and adult learning towards the twin transition

Regions are redesigning their VET and adult learning systems to equip their workforce with skills required for the digital and green transitions. This box presents selected educational programmes, including both public and private initiatives, from OECD regions that equip workers with the technical skills for green jobs, for example in the renewable energy sector.

The Technical Skills for Harmonised Offshore Renewable Energy project (T-Shore) addresses the skill shortages in the offshore wind industry by building and strengthening regional partnerships between VET schools, industry partners and governmental organisations across five countries spread in northern Europe. Through the six Centres of Vocational Excellence (CoVEs), T-Shore aims to provide skills and competencies in five countries, through partnerships with 13 partners. This effort also aims to support regional development by anticipating sector specific skill needs and addressing challenges such as worker mobility, aging workforces, and the need for reskilling. In Denmark, T-Shore helps with the demand to retrain workers form industries such as oil and gas towards offshore wind sector. In the Netherlands T-Shore aimed to address the severe labour market shortages in technical labour, however two CoVEs were established to better reflect the differences between the focus and development of the North and South Netherlands. The North emphasizes supported SME in participating in the offshore wind supply chains, with partners like Noorderpoort and TCNN. In contrast, the South, where the Winddock CoVE was already established in 2018, focuses on innovation in wind turbine maintenance.

In France, regions are launching specialized training centres through public-private partnerships focused on green transition roles, such as the new Douvrin Battery Training Centre in the electric vehicle (EV) sector. The Douvrin Battery Training Centre is a public-private initiative between the region, the Union of Metallurgies Industries (UIMM) Hauts-de-France and Stellantis, an automotive company. The Training aims to prepare and retrain active workers and job seekers for EV roles in a 400 hours training programme. The region plans to train 6 600 workers – compared to a projected 20 000 jobs in the EV sector over the next decade -- to support EV gigafactories and enhance their international competitiveness.

In the Navarra region in Spain, the regional government, in collaboration with the Confederation of Entrepreneurs and the Navarre Industry Association, set up the Training Centre for Renewable Energies and Energy Efficiency skills, CENIFER. The aim is to address skills shortages in the region by offering training adapted to firms’ needs that differs in length usually between 420 and 700 hours spread in an academic year, and some specialization courses include a training module in work centres. It offers a wide range of courses for professions such as technicians in power plants, water management, thermal and fluid installations, solar thermal energy. The trainings range from offerings to the unemployed, to specialized classes offered in the evening to accommodate schedules of those employed.

In the United States, the Workforce Innovation and Opportunity Act (WIOA) Adult Program supports low-income and low-skilled workers transitioning into high-quality, in-demand jobs through reskilling and upskilling since 2014. Each state’s Local Workforce Development Board — composed of representatives of local businesses, labour, community-based organizations and higher education — submits a four-year plan that outlines its strategy for service delivery in its workforce development system. The WIOA funds occupational training and support services, such as employment search assistance, and child care assistance while in training (US Department of Labor, 2024[83]). In 2022, WIOA supported almost 300 000 participants, of which 130 000 also received training services, throughout the United States. 72% of participants were in unsubsidised employment one year after exit from the programme, with a median salary of 8 272 USD among those employed (US Employment and Training Administration, 2024[84]).

Notes: State-level success indicators for the Workforce Innovation and Opportunity Act can be accessed under https://www.dol.gov/agencies/eta/Performance/results (15 October 2024).

Sources: (T-Shore, 2024[85]) (Region Hauts-de-France, 2023[86]) (OECD, 2023[12]) (Educacion Navarra, 2024[87]) (CENIFER, 2024[88]) (OECD, 2023[12]).

For experienced workers, governments can foster life-long learning to give workers the opportunity to adapt to ongoing trends in the labour market. An example of this is RES-SKILL, a European initiative that seeks to equip current and former coal mining industry workers with the skills needed in renewable energy jobs, such as solar photovoltaic installers or wind turbine technicians (RES-SKILL, 2024[89]). The Scottish government’s Adult Learning Strategy 2022 to 2027 aims at creating accessible learning opportunities for adults. The strategy emphasises community-based learning by encouraging local organisations to offer tailored educational programmes to address community needs. It promotes partnerships between government, educational institutions, and businesses to develop and deliver relevant training programmes. This includes addressing digital literacy to ensure all adults can participate in the digital economy and adapt to technological change (Scottish Government, 2022[90]).

Managing migration to ease labour shortages

Migration policies, through both international immigration and promotion of internal migration, can serve as a tool for mitigating labour shortages, with OECD countries such as Canada, Australia, and Germany implementing reforms to facilitate this. Many factors, including the declining working age population across most OECD countries, increase the shortage of high and low-skilled workers, and third-country immigration offers an opportunity to increase the number of adequately skilled workers (Kate Hooper, 2021[91]; Katrin Sommerfeld, 2023[92]). To do this, countries need to look at broadening existing policies, such as easier visa application processes, better recognition of foreign credentials, and by offering a clear path to residency or citizenship (Kate Hooper, 2021[91]; Katrin Sommerfeld, 2023[92]).These can be adapted to the needs of regions that struggle the most with labour shortages.

Some OECD countries implemented regional immigration programs to address labour shortages. The Canadian Atlantic Immigration Program facilitates the hiring process of skilled immigrants for employers who are unable to fill their vacancies in Canada’s four Atlantic provinces (Government of Canada, 2022[93]; Government of Canada, 2024[94]). This program started as a pilot in 2017 and has since filled more than 9 800 vacancies in key sectors experiencing shortages including in health care, manufacturing, accommodations and food services. Over 90% of applicants still live in the region after a year (Government of Canada, 2022[93]). Following the success of this pilot, Canada has announced new immigration pilots to support rural and Francophone minority communities (Government of Canada, 2022[95]). Australia has implemented a similar regional immigration framework through the Designated Area Migration Agreements (DAMAs) that allow regional employers to sponsor overseas workers (Australian Department of Home Affairs, 2024[96]). These agreements are tailored to the specific economic and labour needs of each region, providing a strategic solution to local shortages by attracting skilled migrants to underpopulated areas. Similar immigration policies could be implemented in other OECD countries to tackle labour shortages.

Various OECD countries have introduced direct incentives for internal migration in the form of tax incentives, housing subsidies, and overall regional development. Such policies can contribute to mitigating labour shortages in communities facing declining populations by bringing back workers. An example of such policies is Japan, which projects that by 2040 the population in almost half of its municipalities will decline by 50% or more. To address this, the government implemented its Regional Revitalisation Policy. The policy is aimed at managing population decline in affected regions and at alleviating the concentration of population in the Tokyo area by providing subsidies and incentives to encourage migration from urban to rural areas (Government of Japan, 2019[97]). This includes support for business relocations, housing subsidies, and grants for individuals and families moving to less populated areas (up to YEN 1 million per child) (Government of Japan, 2019[97]). Portugal implemented the Interior Employment Plus - Supported Mobility for a Sustainable Interior" in 2020, which aims to stimulate the competitiveness of regions (Government of Portugal, 2020[98]). Through this policy, workers and teleworkers who move to the interior have access to direct financial support of up to EUR 4 827 including an additional 20% for each member of the household, and moving costs (Government of Portugal, 2020[98]).

Better systems for the recognition of foreign credentials can mitigate labour shortages through labour market integration of third-country immigrants. The foreign credential recognition process (or lack thereof) can be a major roadblock for labour market integration. In Canada, 25.8% of immigrants with a degree completed outside of Canada were overqualified for their job according to 2021 Census data, meaning that over a quarter of all immigrants would have the skills to work in more highly skilled professions (Statistics Canada, 2022[99]). In 2012, Germany introduced the Federal Recognition Act which facilitated non-EU credential recognition. This reform raised employment in regulated occupations, such as nurses, by 18.6% among non-EU immigrants. Furthermore, these immigrants did not have lower skills or receive lower wages than the native-born (Anger, Bessetto and Sandner, 2022[100])

Box 2.15. Building teleworking potential: Ireland, Trento, and The Netherlands Case Studies

Teleworking Strategy in the Autonomous Province of Trento, Italy

Trento implemented a teleworking strategy in 2021 with the goal to lower commuting demands and expand the talent pool by attracting skilled professionals through teleworking opportunities. To achieve this, multiple policies have been put in place, including the creation of teleworking spaces in villages by repurposing buildings and redesigning telecentres. Employers are encouraged to offer flexible work arrangements through hybrid work to allow less frequent commuting and family-friendly arrangements. To promote, encourage and share learnings and best teleworking practices, a community of human resources practitioners has been created.

Ireland’s Rural Development Plan 2021-2025

This plan has been introduced as a post-COVID-19 rural recovery initiative and aims to build the teleworking potential of rural Ireland. This includes optimising internet infrastructure, supporting rural employment and careers, improving public services, revitalising towns, and reaching a climate neutral community. Teleworking is emphasised as it reduces transport emissions, supports local businesses, and offers opportunities for youth employment in their communities. The plan includes investing in teleworking infrastructure to help people remain living in their communities. The plan looks to achieve this to by building 400 teleworking facilities, as well as moving 20% of the public sector to remote work and progressively increasing that percentage. Finally, the plan also explores incentives for relocation to rural towns by providing funding to local authorities to build a strategy to attract talent.

The Netherland’s Flexible Working Act

Adopted in 2016, this legislation encourages flexible working arrangements. Consistently, several cities have undertaken their own initiatives aimed at promoting flexible working arrangements, such as Amsterdam which established the Smart Work Centres as shared office spaces where employees can work closer to their homes. Other cities such as Leeuwarden and Rotterdam developed initiatives for flexible work with the objective of decreasing traffic congestion and air pollution. Nation-wide, the act includes the right to request flexible working hours (allowing for reasoned refusal of the request), and protections against discrimination. The Flexible Working Act applies to all employees and employers regardless of the size or type of organisation.

Source: (OECD, 2023[101])

The rise of teleworking offers a parallel to traditional migration policies, delivering similar economic benefits without the physical relocation of workers. Teleworking has become a common practice among OECD countries and has the potential to alleviate regional labour shortages by allowing firms to tap into a broader range of talent (OECD, 2023[101]). This approach can complement traditional migration strategies, providing a flexible and cost-effective solution to address the growing demand for skilled workers. However, it is important to note the teleworking potential can vary across regions and industries, as it is more common in services than in industrial production. Teleworking potential can be improved by upgrading internet infrastructure, promoting digital skills, fostering automation and establishing regional or sectoral teleworking agreements (OECD, 2023[101]). Additionally, attracting teleworkers working for employers elsewhere may not directly address local labour shortages, but can yield benefits such as increased tax revenue, higher consumption, and expanded professional networks (OECD, 2023[101]).

Policy recommendations to alleviate labour shortages

In conclusion, governments have various policy options to mitigate regional labour shortages. The following place-based recommendations aim at easing labour shortages through AI-induced productivity gains, worker re- and upskilling, talent attraction, support to the economically inactive and the use of labour market intelligence tools.

• Identify opportunities where AI can drive productivity growth in regions: By building local AI capabilities, regions can modernise traditional industries, attract new investments, and harness emerging technologies for sustainable development. This approach can be particularly valuable for regions facing labour shortages, for example driven by demographic pressure.

• Improve the uptake of AI tools across businesses to reap potential productivity gains: Businesses may need additional resources to fully utilise AI tools, such as targeted training programmes, guidance on implementing AI technologies, and workforce retraining. Special attention should be given to SMEs, which often lag behind in technology adoption, by providing the support they need to remain competitive in an AI-driven economy.

• Revise regional skill provisions to address workforce needs: Addressing changing skill needs requires updating the skills provision system to better align with current demands. This includes revising vocational education programmes, expanding access to adult education, and introducing targeted programmes for critical roles in emerging industries.

• Provide tailored reskilling and employment support for displaced workers: Technological advancements, such as AI and automation, lead to shifts in the labour market and may cause worker displacement. Targeted support for these workers, including retraining programs and re-employment assistance, can help mitigate long-term economic losses and prevent vulnerable groups and regions from being left behind amid technological changes.

• Expand training opportunities in non-metropolitan areas: Establishing training opportunities in remote regions can enhance regional attractiveness and help retain young people. By tailoring these programmes to local skill demands, non-metropolitan areas can provide local businesses with a skilled workforce and stimulate economic growth.

• Promote regional talent attraction to address labour shortages: Regions can focus on creating more opportunities to attract skilled workers, such as simplifying administrative processes, recognising relevant qualifications, and providing clear long-term career pathways. Incentives like tax benefits, and housing subsidies can also help draw workers to regions with rising labour demand.

• Increase the participation of hard-to-get groups to alleviate labour shortages: Local economies can tap into untapped potential by removing barriers to employment for groups with traditionally low labour force participation, including women (especially mothers), young people, and older workers. Flexible work arrangements, accessible childcare, targeted training, and incentives for hiring, can support these groups, fostering inclusivity.

• Increase the use of labour market intelligence tools: Advanced intelligence tools provide timely, granular data on regional occupations and skills demand, and can help policymakers understand future needs, as well as the underlying causes of shortages, informing the design of place-based policies. In PES, AI tools can improve job matching, connecting job seekers and employers more effectively, and help anticipate labour shortages.

• Foster collaboration with local stakeholders to strengthen policy intelligence: Engaging local stakeholders, such as employers, educational institutions, and community organisations, provides valuable, real-time insights that can enhance the effectiveness of workforce policies. Such collaboration promotes better-informed, widely accepted, and adaptable policies that respond to regional economic conditions.

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Notes

← 1.  Tradeable services consist of the ISIC 1-digit industries wholesale and retail trade; repair of motor vehicles and motorcycles (G); Transportation and storage (H); Accommodation and food service activities (I); Information and communication (J); Financial and insurance activities (K); Real estate activities (L); Professional, scientific and technical activities (M); Administrative and support service activities (N).

← 2.  Regions with the highest and lowest ICT jobs shortages are, respectively, the top and bottom ICT jobs tightness regions with at least 20% of a country’s employment. The same applies to green jobs in the following paragraph.