This chapter examines the current state of regional labour markets in the OECD, as well as recent and long-term trends in standard indicators such as employment, inclusion and productivity. It also assesses their resilience to the green, digital and demographic transitions. While most regions have recovered since the COVID-19 crisis, regional convergence in employment and participation rates is limited. Despite record-high employment rates in many regions, the inclusion of certain groups in the labour market remains an issue. Labour productivity growth remains modest, and disparities between the most and least productive regions persist. Skill mismatches and non-traditional work are prevalent, and a lack of sectoral diversification may hamper the ability of regions to adapt. Mass layoffs pose additional challenges, especially given their prevalence and severity in some regions.
Job Creation and Local Economic Development 2024
Abstract
The landscape of regional labour markets in the OECD over the past decade reveals a mixed picture of recovery and persistent disparities and challenges in productivity and skills.
Healthy regional labour markets promote economic growth, social inclusion, and overall well-being. This chapter examines their evolution in OECD regions over the past decade and in the aftermath of the COVID-19 pandemic, reporting on recent employment trends, productivity growth, skill polarisation and mismatch, non-traditional work, and sectoral diversification. The analysis sheds light on the diversity of regional experiences and their differing abilities to withstand future shocks and transitions. This diversity highlights the need for differentiated actions given the specific labour market challenges of different types of regions.
Employment rates across OECD regions are at record highs, but significant regional disparities are widespread in some places. In 26 OECD countries, employment rates are above 70% in at least three-fourths of regions. In particular, employment is higher in capital-city regions, regions with a high share of employment in green jobs and tradable sectors, and regions with younger working-age profiles. Almost seven in ten regions recovered both employment and participation rates relative to pre-COVID levels, although the recovery was more widespread for employment rates and in metro regions.
There is little convergence between top- and bottom-performing regions within countries in employment and participation rates or productivity. On average in OECD countries, ten percentage points separate the region with the highest and lowest employment. Participation and employment rates remain about 10 to 13% higher, respectively, in the top versus the bottom quintile of regions in a country.
Many regions still struggle with the labour market inclusion of different groups. Age disparities in regional labour markets widened over the past decade, and gender inequalities narrowed, despite fears that COVID-19 would bring a lasting “she-cession”. The inclusion gap between youth (15-24 year-olds) and the prime-age working population (25-64 year-olds) grew in almost three in five (58%) regions, and the gender inclusion gap fell in five in six (83%) regions over the past ten years, with 20 countries seeing a rise in the age gap and 31 countries seeing a fall in the gender gap in at least 70% of their regions. Capital-city regions exhibit the greatest age disparities in labour force participation rates, while gender disparities are most prominent in non-capital-city regions, by about seven and four percentage points, respectively.
Labour productivity growth remained sluggish over the past decade, with half of OECD regions recording growth rates of less than 0.8% per year. Within-country gaps between the most- and least-productive regions remain large, despite marginally higher productivity growth in lagging regions. In 2022, in two-thirds of OECD countries, productivity in the most productive region is at least 50% higher than in the least productive region. The quintile of regions with the highest initial productivity, within a country, still has 50% higher productivity than the quintile of regions with the lowest initial productivity. Capital-city regions and regions with a higher share of green jobs or jobs specialised in tradable services record significantly higher labour productivity than the national average.
A diversified skills base enhances the quality and resilience of regional labour markets, yet many regions face rising skills polarisation and high skills mismatches. The share of middle-skilled jobs fell in four-fifths of OECD regions, and the share of low-skilled jobs grew in three-fourths of regions where the share of middle-skilled jobs fell. The falling middle may reflect changing labour market demands, but it has not resulted in a better alignment between workers’ skills and those needed by their jobs. Skill mismatches remain an issue for most OECD regions, with significant within-country differences of over ten percentage points in over one-third of countries. Mismatches fell over the past ten years in capital-city regions and in regions with a higher share of green jobs.
The past decade has been marked by profound shifts for global economies. These shifts are driven by rapid technological change, the necessity of environmental sustainability, demographic pressures, and heightened geopolitical instability, all against the backdrop of recovery from the shock of the COVID-19 epidemic. Issues such as labour shortages, sluggish productivity and skill mismatches are increasingly relevant in the context of ongoing transformations such as the green and digital transition, specifically the rise of artificial intelligence (AI). In many places too, the pressures of demographic change, with shrinking working-age populations, are further complicating the situation.
Regional labour markets, given their size, specific characteristics, and degree of specialisation, face distinct challenges in adapting to these megatrends. To keep pace, transitions that build upon a regional labour market’s strength may be necessary to not only navigate these challenges but to actively transform these challenges into opportunities. This chapter provides an overview of these recent trends in regional labour markets.
Employment rates stand at a record high across OECD countries, even as the regional picture is more uneven. The average employment rate (share of the working-age population in employment) in the OECD reached over 70% in Q2 2024, surpassing this figure in almost two-thirds of OECD countries. These historic employment gains create benefits across demographic groups (OECD, 2024[1]). Most national labour market indicators, such as employment, unemployment, and participation rates, have recovered in most countries following the shock of the COVID-19 pandemic. For example, by Q2 2023, unemployment and inactivity rates were, on average, half a percentage point and one percentage point, respectively, below pre-pandemic levels in OECD countries (OECD, 2023[2]). However, this pattern has not been true for all regions, contributing to persistent or growing regional inequalities in some countries. Employment rates fully recovered or exceeded their pre-pandemic levels by Q2 2022 in less than half of OECD regions across 33 countries (OECD, 2023[3]).The chapter will address the current situation and report on recovery into 2023.
Labour shortages remain a prevalent and persistent issue in this high-employment context. As industries evolve and new technologies emerge, the demand for specific skills can sometimes outpace their supply, making it difficult for firms to fill needed positions. It may also be that available jobs are not attractive enough in terms of pay, working conditions, location, or a combination of these. Even if 2023 saw real wage growth, wage gains remain below pre-pandemic levels (OECD, 2024[4]). This issue is further exacerbated by demographic change, as ageing populations combined with declining birth rates contribute to shrinking working-age populations. Regional labour market tightness increased by around 50% since 2019, affecting regions with high and low prior levels of shortages similarly. At the same time, the average difference between the relatively tightest and least tight regions is almost twice the national average, indicating substantial regional variation in the extent of labour shortages (see Chapter 2).
Labour productivity is an important driver for reducing income inequality, yet regional differences remain large: levels in the most productive region are almost twice as high as the least productive region, on average within OECD countries (OECD, 2023[3]). The trend of stagnating labour productivity adds to the challenge of widening regional inequalities in GDP per capita in more than half of OECD countries with available data. This is especially a challenge where large differences in productivity levels exist even where there were declines in GDP per capita inequality (OECD, 2023[3]).
Active innovation and the diffusion of new technologies, for example, artificial intelligence, across regions, coupled with targeted investments in infrastructure such as digital technologies may be avenues for boosting productivity (OECD, 2023[3]). Yet, whether artificial intelligence will support or replace workers depends on the "task-based" nature of jobs and the ability of AI to perform those tasks more efficiently (Nedelkoska and Quintini, 2018[5]). The issue is addressed in Chapter 3 through new estimates on occupational exposure to AI, such as large-language models. The regional-level analysis presents within-country disparities in AI exposure, affecting a broader group of people and places. It discusses the potential double-edged sword of the integration of AI in the workforce: whether it is productivity-boosting or leads to job displacement.
Overall, the rapid pace of technological change and the shift towards greener economies creates a growing need for new skills and competencies and regions have different capacities to adapt. For example, while 18% of workers in the OECD have jobs with a significant share of green tasks that promote environmental sustainability, the share of these “green-task” jobs shows a considerable range across regions, from 7% to more than 35%, and are especially concentrated in capital-city regions (OECD, 2023[6]).
In light of these trends, this chapter examines the current state of regional labour markets and their resilience to major transitions. The first section explores regional labour market dynamics over the past decade, touching upon the recovery from the COVID-19 crisis, and regional convergence. It considers recent developments and implications for employment, labour productivity and inclusion. The second section delves into different indicators linked to the ability of regional labour markets to adapt and benefit from the full potential of both workers and firms. For example, it investigates recent trends in the take-up of non-traditional work, skills polarisation and skills mismatch, as well as the sectoral concentration of employment and the incidence of mass layoffs.
Across OECD regions, employment rates have reached record highs (Figure 1.1). In 26 OECD countries, employment rates are above 70% in at least three in four regions, and in 20 countries, in all regions. In almost three in five regions (59%), employment stands at over 70% of the labour force and over 80% in almost one in ten regions (8%). Employment is particularly high in Iceland, Switzerland, and the Netherlands, which all boast at least two regions in the top ten of employment rates across OECD regions. Four out of the top ten regions are in the Netherlands, which can likely be attributed to the high take-up of part-time work. On the other hand, the employment rate is lagging and below 60% in at least one-third of their regions for seven OECD countries. This is also the case in about one in seven (14%) OECD regions. This is a particular challenge for Türkiye and Italy, which have six (out of 26) and three (out of 21) regions, respectively, in the bottom ten of employment rates across OECD regions. The lowest employment rate is in Chocó (Colombia), likely due to its geographical isolation and lack of infrastructure. This is similar to the challenges faced by the Italian regions, which are all located in the south. While in Türkiye, low female labour market participation rates, which range from 24% to 49%, limit employment rates.
Within-country regional differences are widespread. In 16 out of the 35 countries with more than one region, the difference between the top and bottom regions in employment rates is over ten percentage points. In Colombia, the difference exceeds 34 percentage points; in Italy, it is almost 30 percentage points and in Türkiye and Israel, almost 22 percentage points, highlighting particularly large disparities in these countries. Portugal has the smallest dispersion, among countries with at least five regions, at about two percentage points. This is followed by Denmark and Norway, where the difference is about 3 and 3.6 percentage points, respectively. As many regions experience all-time high employment, the focus is shifting towards widespread labour shortages (see Chapter 2).
Note: The figure shows the regional dispersion (highest, lowest and median value) in the employment rate for 15-64 year-olds in 2023. For Colombia, the data refers to 2022 due to data availability. The sample is all TL-2 regions in countries (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Over the past decade, there has been minimal progress towards regional convergence in employment rates. In 2023, employment rates in the top quintile of regions in a country were more than 4% above the national median versus almost 6% in 2013 (Figure 1.2). In contrast, employment in the bottom quintile of regions is almost 7% below the national median, versus almost 8% in 2013. Thus, there have been small decreases in within-country disparities over the past ten years. In 2013, the top quintile of regions had employment rates almost 16% higher than the bottom quintile, with the gap falling to 13% by 2023. Overall, the within-country difference in employment rates between top and bottom regions fell by about 2.5% of the national average over the past ten years.
Evolution of the employment rate relative to the national median, 2013 to 2023
Note: The figure shows the evolution of the employment rate for the working-age population (15-64 year-olds), relative to the national median (which corresponds to 100 on the top graph), for the top and bottom 20% of regions which account for at least 20% of the population in a country. The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Regions with younger age profiles (non-ageing regions), capital-city regions and regions where employment is specialised in green jobs or tradeable sectors lead employment rates (Figure 1.3). The strongest predictor of high employment rates is the absence of an increase in the old-age dependency ratio: within-country employment levels are close to 4.5 percentage points higher, on average, in those regions than regions where the old-age dependency ratio rose. This is followed by the regional employment structure. In regions with an above-country-median employment share in green jobs or in tradeable goods or services, the employment rate is about 2.5 percentage points higher, on average, than in regions with a below-median share of green jobs or specialised in non-tradeable sectors. Finally, capital-city regions have employment rates that are two percentage points higher than non-capital-city regions. These regional differences in employment rates exist regardless of national characteristics or population size and account for country-level shocks. In contrast, none of these regional characteristics is correlated with within-country employment growth over the past ten years (Annex Figure 1.B.1). Regional characteristics in the same country, such as demographics, location or industry structure, are thus important for understanding current employment rates.
Within-country correlation of the employment rate to selected characteristics, 2023 or latest available year
Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. The graph shows the coefficient and 90% confidence intervals of separate multivariate regressions of the employment rate in 2023 (for Colombia, the latest available year is 2022) on a dummy for capital-city regions, ageing regions (defined as those that experienced an increase in the elder-dependency rate over the past five years), for an above national median employment share in green jobs in 2021, in tradeable services (ISIC broad sectors G to N), tradeable goods (ISIC sectors B, D, E) or neither tradeable goods nor services. The coefficient (`within-country correlation') presents the within-county percentage point difference in employment rates based on the characteristic on the x-axis. Each regression also controls for the log of population in 2023 or latest available year and country fixed effects. The level of observation is the TL-2 region. The sample of countries includes all OECD countries. Robust standard errors are clustered at the country level. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD elaboration based on the OECD Regional databases.
Despite concerns that COVID-19 would exacerbate regional inequalities through substantial and sustained workforce dropout in more affected regions, within-country differences in participation rates have been stable (Figure 1.4). Participation rates refer to the share of people employed or looking for work out of the working-age population. A fall in participation rates implies that people are dropping out of the workforce into economic inactivity, i.e. they are no longer employed or looking for work. There was a particular risk of this during the COVID-19 crisis due to its specific nature, characterised by lockdowns, challenges to specific industries, risks to older workers, and an increased burden of home care. Thus, it is notable that the ratio of participation rates in the top compared to the bottom quintile of regions in a country is at its lowest level over the past ten years: about 9.9%, which is a modest decrease from 11% in 2013. This is despite the small (0.6 percentage points) increase in the ratio during 2020. Overall, in 2023, the top quintile of regions had participation rates 3.5% higher than the national median, while the bottom quintile of regions trails the national median by 5.5%.
Evolution of the participation rate relative to the national median, 2013 to 2023
Note: The figure shows the evolution of the participation rate for the working-age population (15-64 year-olds), relative to the national median (which corresponds to 100 on the top graph), for the top and bottom 20% of regions which account for at least 20% of the population in a country. The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period. The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Employment and participation rebounded strongly after the COVID-19 shock
By 2023, half of OECD countries (19 out of 38 countries) saw all regions recover their employment levels to at least pre-pandemic levels (Figure 1.5). In ten countries, employment rates surpassed pre-pandemic levels by more than 1.5 percentage points in all regions, while in eight OECD countries, employment recovery is limited, and no region rebounded by more than 1.5 percentage points. In only one country (Latvia), all regions have yet to recover to their pre-crisis employment rate; in all other countries, regional employment recovery is uneven with some regions recovering while others lag. Overall, more than three-fourths (76%) of OECD regions recovered their pre-pandemic employment rates, with almost half (49%) of regions recovering by at least 1.5 percentage points, and almost one in ten (8%) regions by over 5 percentage points. In contrast, for one in ten OECD regions, employment rates are still more than 1.5 percentage points below pre-crisis levels, and for a little over one in a hundred (1.2%) OECD regions, rates lag by more than 5 percentage points. While the vast majority of OECD regions showed strong employment recovery after the COVID-19 shock, some still face challenges in regaining their pre-pandemic employment levels.
Note: The figure shows the regional difference between the employment rate in 2019 and the employment rate in 2023, except for Colombia where the latest available year is 2022. The sample is all TL-2 regions (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Employment rates returned to pre-crisis levels for metropolitan regions in 12 countries and for non-metropolitan regions in 11 countries, out of the 18 countries with available data (Figure 1.6). Yet, employment in metropolitan regions rebounded by 0.3 percentage points more than in non-metropolitan regions on average, ranging from -3.2 percentage points in Lithuania (since non-metropolitan regions recovered the quickest) to over 5 percentage points in Ireland. Furthermore, although the average employment change is similar across metropolitan and non-metropolitan regions (about 1.1 and 0.9 percentage points, respectively), non-metropolitan regions showed much greater volatility with greater employment gains and employment declines than metropolitan regions. Thus, despite the increased severity of the COVID-19 epidemic in cities and metropolitan areas, metropolitan regions proved to be more resilient and stable in responding to the pandemic-related labour market shock.
Median employment rate change, 2019 to 2023 or latest available year
Note: The figure shows the change in the employment rate from 2019 to 2023 (or the latest available year after 2020), in metropolitan regions and non-metropolitan regions, defined at the TL-3 level, for each country. For Germany, the latest available year refers to 2021, and for Czechia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom, to 2022. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Most regions recovered strongly from the COVID-19 pandemic both in employment and labour market participation. Compared to pre-COVID levels, almost seven in ten (69%) OECD regions reached at least pre-crisis levels in both dimensions, with over one-third (36%) of regions displaying improvements in both rates by over 1.5 percentage points (Figure 1.7). Yet, regional disparities exist. More than one-third of OECD countries have regions that recovered in only one dimension. And in one in nine (11%) OECD regions, both participation and employment rates are at least 1.5 percentage points below their pre-Covid values. The average employment change since the pandemic across all OECD regions is 1.5 percentage points, while for participation rates, the change is 1.1 percentage points.
In general, regional employment rates recovered more widely than participation rates. This suggests that employment rates, if viewed in isolation, may paint a slightly more positive picture since they obscure the presence of economic inactivity, i.e. the working-age population that is neither employed nor looking for work. Particularly, in almost half (45%) of OECD regions, employment rates recovered by more than participation rates; and in one in twelve (9%) regions, only employment rates recovered, more than double the regions where only participation rates recovered. Among regions that have yet to recover, the average gap (the extent to which the current rate is below the pre-crisis value) is similar: about 1.6 percentage points for both participation and employment rates. The policy response of countries to COVID-19, particularly through employment support programmes, was without precedent, matching the novel nature of the shock (OECD, 2022[7]). Yet, given that labour market participation rates lagged employment rate recovery, additional efforts may be required to bring back workers who left the labour market during COVID-19.
Note: The figure shows the share of regions in each country which belong to each of the five categories comparing the change in employment and participation rates from 2019 to 2023, except for Colombia where the latest available year is 2022. `Faster for employment' means that the employment and participation rate are at or above their 2019 rate, but employment recovered by more. `Faster for participation' means that both the employment and participation rates are at or above their 2019 rate, but participation recovered by more. `Only employment' means that the employment rate is at or above the 2019 rate, but not the participation rate. `Only participation' means that the participation rate is at or above the 2019 rate, but not the employment rate. `Neither' means that both the employment and participation rates are below their 2019 rate. The sample is all TL-2 regions in all OECD countries (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds. The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Government action likely played a key role in the post-pandemic recovery patterns of employment and participation, but no particular scheme stands out for explaining regional recovery. These policies included job retention schemes such as short-term work (STW) schemes, wage subsidies, and cash transfers (see Box 1.1 for an overview). Furthermore, government action, particularly furlough schemes, likely avoided large negative shocks to employment (Calligaris et al., 2023[8]). Yet, some workers who are more represented in certain regions, for example, the self-employed, were often not eligible to benefit from these schemes in many countries. Differences in accounting techniques, particularly whether furloughed workers were counted as unemployed, as in North America, or employed, as in Europe, also obscure the ability to comment on their efficiency. It is also important to note that regions may have been exposed to other shocks in the same period, which are not considered here. For example, the energy crisis triggered by Russia’s war of aggression against Ukraine likely affected the recovery of European regions to different degrees, depending on their reliance on Russian gas supplies (OECD, 2022[9]; OECD, 2022[10]).
Regions experienced narrowing gender inequalities and widening age inequalities over the past ten years
In response to the unprecedented shock of the global pandemic in 2020, countries implemented or expanded employment support policies to sustain economies and workers. These policies include the use of job retention schemes in the form of short-term work (STW), wage subsidies, and cash transfers to mitigate the economic impact of the shock. Both existing and temporary policies were expanded during the pandemic to allow for increased access, coverage, and generosity (OECD, 2020[11]). These policies aimed to mitigate the effects of the crisis, prevent mass layoffs, maintain attachment to the workforce, and support a quick recovery after public health restrictions were lifted.
Most OECD countries had pre-existing job retention schemes (JRS), while others implemented temporary policies. Examples include job retention schemes such as STW and expanded benefits, while others introduced temporary wage subsidies and direct cash transfers to cover independent workers. The STW schemes are temporary partial or full suspensions of work contracts that directly subsidise hours not worked. Wage subsidy schemes do not necessarily reduce the working hours of workers but rather subsidise hours worked and can also supplement the earnings of workers on reduced hours. Most also provided cash transfers to help workers not covered by traditional job retention schemes (OECD, 2020[11]).
STW schemes existed in most OECD countries and were expanded during the crisis, with some regional differences in take-up rates. This was the case in Belgium, France, Germany, the United Kingdom and the United States. Otherwise, other OECD countries, such as Australia, Canada and Colombia, used temporary wage subsidies instead of STW schemes to maintain employer-employee relationships.
Countries also rolled out innovative cash transfers to aid workers, adapting these support measures dependent on earnings, or offering cash transfers at a flat rate or value to the entire eligible population. These countries include Austria, Canada, Italy, Japan, Korea, the United States and the United Kingdom.
Across the OECD, JRS supported over 50 million jobs, ten times as many as during the global financial crisis of 2008-10 (OECD, 2020[12]). However, there are significant regional disparities within countries in the participation rates in these programmes. These are likely the result of local economic structures as well as geographically targeted containment measures. Unfortunately, the lack of comprehensive data on the regional take-up of these schemes prevents any analysis of whether regional take-up explains why some regions performed better during the crisis. It is also important to note that while most EU countries had pre-existing JRS, many other OECD countries such as Australia, Canada, and the United Kingdom, had to quickly implement a temporary JRS, often in the form of wage subsidies.
The labour market experience of workers during the past decade has varied depending on their demographics, with distinct challenges and responses shaping their paths (OECD, 2022[7]). The recovery from the recent pandemic is especially pertinent in this regard. For example, older workers, given the health risks during the COVID-19 crisis, may have chosen to permanently exit the labour force, such as taking early retirement. For the cohort of young workers who entered the labour market during the crisis, the shock could have had a lasting impact on their labour market integration and career progression. Lastly, lockdown restrictions put further pressure on child and home care activities, the brunt of which tends to fall on women, bringing down their participation rates (Djankov et al., 2021[13]; OECD, 2021[14]; Alon et al., 2020[15]). The aggregate picture of the post-crisis recovery of labour force participation masks the diversity of these experiences. Consequently, analysis of differences in labour market outcomes by gender and age warrants specific attention.
The past decade in OECD regions witnessed a narrowing of gender inequalities in many labour markets. In five out of six (83%) OECD regions, the gender gap in participation rates, which reflects the difference between male and female labour market participation rates, fell from 2013 to 2023 (Figure 1.8). In more than two-thirds (67%) of regions, the gap fell by more than 1.5 percentage points. In 31 countries, gender gaps fell in at least 70% of their regions. And, in 18 OECD countries, the gender inclusion gap fell in all regions. While no OECD countries saw the gender participation gap increase in all regions, some countries (Chile, Colombia, Costa Rica, Greece, Mexico, New Zealand, Türkiye and the United States) show significant regional disparities in the change in the gender gap of over ten percentage points. For example, in Türkiye and New Zealand, over 21 and 17 percentage points separate the region with the greatest reduction in the gender gap (Western Black Sea Middle and East in Türkiye and Gisborne Region in New Zealand) and the greatest increase (Eastern Anatolia - West in Türkiye and Tasman-Nelson-Marlborough in New Zealand). On average, the gender inclusion gap fell by 2.7 percentage points across OECD regions.
Over the same period, age disparities increased by 0.9 percentage points, on average, across OECD regions. The age inclusion gap, or the difference between participation rates of the prime-age (25-64 year-olds) working population and youth (15-24 year-olds), grew in almost three in five (59%) of regions, growing significantly by over 1.5 percentage points in more than half (53%) of regions (Figure 1.8). In 23 OECD countries, age gaps grew in at least 70% of their regions. All regions in 13 OECD countries saw increases in the age inclusion gap. In four countries, all regions decreased their age inclusion gap. Of the remaining countries where the change in the age inclusion gap did not change in the same direction for all regions, in Chile, France, Mexico, New Zealand, Slovak Republic, and the United States, the difference between the region with the greatest increase and the region with the greatest reduction is over twenty percentage points. It is important to caveat these findings as the labour force participation rates of youth are not adjusted to account for the general trend of young people deciding to remain longer in higher education.
Share of regions given change in the gap in the participation rate by age and gender, 2013 to 2023 or latest available year
Note: The figure shows the share of regions in each country which belong to each of the four categories representing the difference between the age gap between the prime-age working population (25-64 year-olds) and youth (15-24 year-olds) (top panel) and gender gap between male and women (bottom panel) in the participation rate in 2013 and the gap in the participation rate in 2023. For Colombia (except for Chocó where the data refers to 2010 to 2020), Korea and the United Kingdom (except for North where the data refers to 2011 to 2021); for Mexico (except for Nayarit where the data refers to 2009 to 2019) the last year is 2020. The sample includes all TL-2 regions in countries with available data, including the OECD accession countries of Bulgaria, Croatia and Romania. The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population in the same subgroup, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Rising age disparities cannot solely be attributed to rising educational enrolment rates; they are also not associated with increases in the NEET rates (Figure 1.9). While a positive correlation between rising youth enrolment rates and increases in the age gap in participation rates exists across regions, the association is small (0.02) and weak (not statistically different from zero). Thus, widening age disparities cannot be entirely attributed to a general trend of staying longer in education. At the same time, the share of youth not in employment or education (NEET) fell over the past ten years in almost nine in ten (88%) OECD regions with available data, even as age disparities widened.1 Furthermore, a time-series analysis of the evolution of the age and gender gap in employment and participation rates suggests that age disparities grew during the pandemic, while men and women were similarly affected (Annex Figure 1.B.2).2 These pieces of evidence suggest that, despite the rise in the age gap in participation rates, there is likely a limited risk of further labour market scarring, whereby youth who graduate during a recession or shock suffer long-term negative effects on their earnings and career opportunities over time (Tomlinson and Tholen, 2023[16]; Schwandt and von Wachter, 2019[17]). Yet, it is important to continue to monitor the situation. These evolving patterns underscore the need for targeted policies to address age inequalities where they persist while supporting the gains achieved in gender balance (for selected examples, see Annex Box 1.A.1. in Annex 1.A).
Correlation between the ten-year change in the NEET rate (left) or the youth enrolment rate (right) and the age inclusion gap, 2013 to 2023 or closest available years
Note: The figure shows the ten-year change in the difference in the participation rate for the prime-age working population (25-64 year-olds) and youth (15-24 year-olds) on the y-axis, and the ten-year change in the youth not in employment, education or training rate (18-24 year-olds) on the x-axis on the left graph and the ten-year change in the educational enrolment rate for youth (20-29 year-olds) on the x-axis on the right graph, over the years 2013 to 2023, or the closest available years for OECD TL-2 regions with available data. For the age inclusion gap, the last available year is 2022 for regions in Colombia (except for Chocó where the data refers to 2010 to 2020), Korea, and the United Kingdom (except for North where the data refers to 2011 to 2021); for Mexico (except for Nayarit where the data refers to 2009 to 2019) the last year is 2020. For youth enrolment rates, the ten-year period is 2012 to 2022 for Colombia, Latvia, New Zealand, and the United States; the initial year is 2014 for Estonia and Poland, and 2016 for Chile and Korea; for Austria, Australia, Belgium, Czechia, Denmark, Germany, Greece, Spain, Finland, France, Hungary, Italy, the Netherlands, Norway, Poland, Portugal, Sweden, Slovak Republic, Switzerland, Türkiye, and the United Kingdom, the last available year is 2022. For NEET rates, the ten-year period refers to 2011 to 2021 for Sweden, to 2012 to 2022 for Australia, Belgium, Israel, Japan, Mexico, Slovak Republic, Spain, Switzerland, the United Kingdom and the United States. The dotted line represents the correlation line, and the grey-shaded area represents the 95% confidence intervals between the two measures. The estimate of the correlation is listed on the top right of each graph with standard error in paratheses. Each dot represents a TL-2 region. Outliers, defined as regions with values in the top or bottom 8% of the distribution, are not included. The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population in the same subgroup, where the working-age is defined as 15-64 year-olds. The NEET rate is defined as the share of youth not in employment, education or training) out of the youth working-age population (15- 24 year-olds). The educational enrolment rate is the share of individuals aged 15-29 year-olds enrolled in all types of schools and education institutions, including public, private and all other institutions that provide organised educational programmes according to the ISCED 2011 classification, regardless of education level, out of all individuals aged 15-29 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Age disparities in labour force participation rates are highest for capital-city regions, while gender disparities are most prominent in non-capital-city regions (Figure 1.10). In 2023, capital-city regions had a gender inclusion gap that was almost four percentage points below that of non-capital-city regions. In contrast, the age gap in participation rates was almost seven percentage points higher in capital-city regions. Both types of regions exhibit the same general trend over the past years in the evolution of the age and gender gap in participation rates: the age gap is increasing over the decade while the gender gap is decreasing. Yet, in both cases, disparities between capital and non-capital-city regions are widening. From 2013 to 2023, they increased marginally by 0.3 percentage points for the gender inclusion gap and by 2.8 percentage points for the age inclusion gap. Universities, and thus, a higher share of students, tend to be located in capital-city regions, which likely explains a part of the increase, as previously discussed.
Note: The figure shows the evolution of the difference in the participation rate for youth (aged 15 to 24) and the prime-age population (top panel) and the gender difference for males and females (bottom panel) for the working-age population (15-64 year-olds) for capital-city regions vs non-capital-city regions. The sample is all TL-2 regions in OECD countries with data available over the entire time period. The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population in the same subgroup, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Labour productivity, defined as output per worker, is often cited as a primary driver of growth, well-being, and competitiveness in the global economy (see Box 1.2). It plays a role in enabling higher wages, improved living standards, and increased investments in public services and infrastructure. As economies globally face the challenges of technological adaptation and demographic shifts, understanding the dynamics of their productivity allows regions to adapt to these challenges. Factors such as technological innovation, human capital, regulatory environments, and infrastructure development play significant roles in shaping productivity trends. However, recent years have witnessed a troubling slowdown in productivity growth across various advanced economies, accompanied by a disconnect between productivity gains and real wage increases. This scenario underscores the need for insightful policy interventions that can revive productivity while aiming for benefits that extend to workers and enhance the economic well-being of regions.
Labour productivity, measured here as the output per worker, is a fundamental indicator of economic efficiency and a key driver of economic growth and well-being.1 Higher labour productivity allows for increased wages and living standards since it implies more value is generated per worker or hour worked. This, in turn, can support higher income levels and greater investments in public services and infrastructure (Abiad, Furceri and Topalova, 2015[18]). On a global scale, productivity improvements allow regions to maintain their competitiveness, as they reflect an economy’s ability to innovate and adapt to technological advancements (OECD, 2018[19]; Schwab and Zahidi, 2020[20]). Consequently, a focus on labour productivity growth is vital for driving local economic development and improving the quality of life in OECD regions.
Productivity growth is driven by a confluence of factors, including technological advancements, human capital development, regulatory environments and infrastructure improvements (Syverson, 2011[21]). Technological innovation, particularly in the digital and manufacturing sectors, has been identified as a key enhancer of productivity by enabling more efficient production processes and fostering new business models (OECD, 2024[22]; OECD, 2023[23]). For example, there is early micro-evidence that artificial intelligence, such as large language models, may be productivity-enhancing, although long-run aggregate effects are not yet evident (Filippucci et al., 2024[24]). Its impact on workers and occupations is also not yet clear (see Chapter 3). Human capital also plays a role: better-educated workforces adapt more swiftly to new technologies and processes, thereby increasing output (OECD, 2019[25]). In addition, regulatory frameworks that encourage competition and facilitate fair market conditions can significantly boost productivity by promoting efficiency among businesses (Nicoletti and Scarpetta, 2003[26]; Rubens, 2023[27]). Lastly, investments in infrastructure not only improve efficiency but also connect markets more effectively, enhancing productivity at both national and regional levels (OECD, 2023[3]).
Despite its central role in driving economic growth and well-being, productivity growth slowed down significantly in recent years, turning negative in the European Union, the United States and the OECD in 2022 (OECD, 2024[28]). Several potential, and likely inter-linked, explanations lie behind this productivity slowdown rooted in macroeconomic, societal and technological shifts. For example, one theory relates to the slowdown in technological progress given the increasing difficulty of generating new ideas and fewer groundbreaking innovations (such as electricity and the internal combustion engine) versus advances in software and information technology (Bloom et al., 2020[29]; Gordon, 2017[30]). Structural factors, such as ageing populations, the slowdown in trade and lower growth of allocative efficiency, also play a role (Goldin et al., 2024[31]; Maestas, Mullen and Powell, 2023[32]; Daniele, Honiden and Lembcke, 2019[33]). Skills mismatches and inadequate investment in education and training, depressing the contribution of human capital, may have also hindered productivity improvements across various industries (World Bank Group, 2021[34]). Finally, difficulties in measuring labour productivity may also be a factor, albeit a small one that cannot fully explain the widespread phenomenon (Ahmad, Ribarsky and Reinsdorf, 2017[35]).
An important caveat is the documented trend of the decoupling of productivity and real wage growth, which casts doubt on the ability of productivity gains to translate into improvements in well-being. A study across 24 OECD countries found that productivity increases decoupled from gains in real wages over the period 1995 to 2015, given declines in total-economy labour shares and a partial measure of wage inequality (the ratio of median wages to average wages) (Schwellnus, Kappeler and Pionnier, 2017[36]). Increase in knowledge-based capital, technological change, and the rise of global value chains and income inequality are all cited as potential drivers of this phenomenon (Autor and Salomons, 2018[37]; Berlingieri, Blanchenay and Criscuolo, 2017[38]). Policies that call for higher minimum wages, unionisation, employer protection laws and reduced wage inequality may contribute to a positive link between productivity and wages over time, as well as to encourage upskilling of workers to reduce capital-labour substitution (Berlingieri, Blanchenay and Criscuolo, 2017[38]; OECD, 2018[39]). Many of these factors are likely to depend on regional characteristics; for example, gains from knowledge-based capital are likely to accrue in metropolitan regions.
1. Labour productivity can also be defined as output (real gross domestic product (GDP)) per hour worked. This would account for the overall trend of falling working hours across OECD countries, although there are some exceptions, for example, in Turkey, Mexico and Colombia. Over the past two decades, the average annual hours worked per worker decreased, due to changes in work patterns, increased part-time work and shifts towards service industries (OECD, 2021[88]). Nonetheless, the same general pattern of sluggish labour productivity growth is observed also with this definition (OECD, 2024[28]).
Within-country productivity differences are widespread, driven by a few top-performing regions. The most productive region is twice as productive as the least productive, on average (Figure 1.11). Labour productivity in the most productive region is more than three times higher than in the least productive regions in 3 out of 33 (9%) OECD countries and twice as high in 20 out of 33 (60%) OECD countries with available data. This is mainly driven by a few regions that lead productivity within a country. In 30 out of 33 OECD countries, the median relative productivity level is below the national average, which is normalised to one. Overall, almost two-thirds (62%) of OECD regions have productivity levels below the national average.
Note: The figure shows the regional dispersion (highest, and lowest value) for labour productivity, relative to the regional median in the country for 2022 or the latest available year. The vertical line represents the national average, normalised to one. The data refers to 2021 for New Zealand, Norway, Switzerland and the United Kingdom; to 2020 for Australia and Alaska (United States); and to 2018 for Nunavut, N.W. Territories, and Yukon regions in Canada. The sample is all TL-2 regions in countries (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data, excluding Ireland.
Source: OECD calculations based on the OECD Regional databases.
In half of OECD regions, labour productivity growth over the past decade was below 0.8% per year (Figure 1.12). Labour productivity growth, nonetheless, varies widely across OECD regions, ranging from a decrease of about 5% in Greece’s Western Macedonia region to increases of almost 6% in Chile’s Los Lagos region and almost 5% in several regions in Turkey (Southern Aegean; Western Black Sea – West; and Northeastern Anatolia - East). Within-country differences are also significant among OECD countries. The median regional dispersion, i.e. the difference between the top and bottom region, in annual productivity growth over the past ten years is about 1.4 percentage points. The highest dispersions were observed in Chile (8 percentage points), and Greece (6 percentage points). The lowest dispersions were observed in Belgium (0.5 percentage points) and Sweden (0.8 percentage points). In more than half of the countries with available data and with more than three regions, regional dispersion is above two percentage points.
Note: The map shows the initial labour productivity in 2012 and the annual rate of labour productivity growth over the past ten years, from 2012 to 2022 or the closest available years. The initial year refers to 2013 for Chile. The last year refers to 2021 for New Zealand, Norway, Switzerland and the United Kingdom; to 2020 for Australia and Alaska (United States); and to 2018 for Nunavut, N.W. Territories, and Yukon regions in Canada. Initial productivity levels are shown through the size of the circles and the change in labour productivity is shown through the colour scale. Labour productivity is measured as GDP (in USD 2015 PPP) per worker, using regional deflators. The sample is all TL-2 regions (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data, excluding Ireland.
Source: OECD calculations based on the OECD Regional databases.
Annual labour productivity growth, within countries, evolved similarly over the past decade (2012 to 2022) for regions with higher and lower levels of productivity, even if the least productive regions grew marginally faster (Figure 1.13). Between 2012 and 2022, productivity growth in the least productive regions grew faster than the least productive in all but two years. For most of the decade, the least productive regions grew, on average, at an annual rate of about 0.9%. During this time, the most productive regions experienced slightly more sluggish growth of 0.6%, on average, or even negative growth, as in the years 2012 and 2020. Indeed, in 2020 in the aftermath of the onset of the COVID-19 pandemic, both groups of regions experienced negative annual productivity growth, with the most productive regions seeing a downtick of almost 1.3 percentage points more than the least productive regions. Productivity growth in the most and least productive regions grew sharply and converged in 2021, with a less than 0.5 percentage point difference in annual growth rates. This may be due to the shifting composition of employment during the crisis, where smaller firms and lower-skilled workers dropped out of the labour market, mechanically raising the average (Kapsos, 2021[40]). In the most recent year, 2022, the most productive regions overtook the least productive regions in terms of growth. The gap in labour productivity growth reached 0.7 percentage points, mostly due to a decline in labour productivity growth in the least productive regions of almost 2.8 percentage points. This shift may represent a temporary fluctuation as the economy stabilised from the COVID-19 shock and distortions caused by furloughed workers are no longer as prevalent, or it may represent a longer-term trend that should be monitored.
Annual labour productivity growth given initial productivity level, 2012 to 2022
Note: The figure shows the evolution of the annual growth rate of labour productivity for the top and bottom 20% of regions within a country from 2012 to 2022 based on initial productivity levels in 2012 which account for at least 20% of the population. The sample is all regions in countries with at least five regions and with data available over the entire time period, excluding Ireland.
Source: OECD calculations based on the OECD Regional databases.
Productivity gains among the least productive regions were not enough to significantly narrow the gap between the top and bottom-performing regions. By 2022, productivity levels are almost 53% higher in regions in the top quintile of productivity than regions in the bottom quintile, compared to almost 56% in 2012, a decrease of less than two percentage points (Figure 1.14). Thus, although the least productive regions have marginally higher annual productivity growth, these gains do not show up in terms of relative productivity gains. The most productive regions have productivity levels almost 30% above the national median, on average, while the least productive regions have productivity levels that are almost 13% below the national median in 2022. This represents a difference of 41% of the national median, on average. The gap in productivity levels between the best- and worst-performing regions within a country highlights persistent regional inequalities within the OECD. While some regions are thriving with high productivity levels, others are struggling significantly, which could exacerbate socio-economic disparities.
Evolution of the labour productivity relative to the national median, 2012 to 2022
Note: The figure shows the evolution of labour productivity, relative to the national median (which corresponds to 100 on the top graph), for the top and bottom 20% of regions which account for at least 20% of the population in a country. The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period, excluding Ireland.
Source: OECD calculations based on the OECD Regional databases.
Productivity levels are higher in capital-city regions and regions with a higher share of green jobs or specialising in tradeable services (Figure 1.15). Regions that contain the capital city have higher productivity, far above non-capital-city regions, by over 1.7 standard deviations from the national mean. The industrial composition of employment also plays a role. In regions with an above-median share of green jobs or where employment is specialised in tradeable sectors, labour productivity is higher by almost 0.8 standard deviations from the national mean. In contrast, none of these characteristics are correlated with higher labour productivity growth (Annex Figure 1.B.5).
Within-country standardised correlation of labour productivity to selected characteristics, 2022 or latest available year
Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. The graph shows the coefficient and 90% confidence intervals of separate multivariate regressions of labour productivity, standardised within each country, in the latest available year on a dummy for capital-city regions (large regions that include the capital city), regions with an above national median employment share in green jobs in 2021, in tradeable services (ISIC broad sectors G to N), tradeable goods (ISIC sectors B, D, E) or neither tradeable goods nor services. The coefficient represents the change in productivity, measured in standard deviations from the national mean, for regions with the specified characteristic on the x-axis. Each regression also controls for the log of population in the latest available year, a dummy for the latest available year, and country fixed effects. The level of observation is the TL-2 region. The sample of countries includes all OECD countries with available data, excluding Ireland. The data refers to 2021 for New Zealand, Norway, Switzerland and the United Kingdom; to 2020 for Australia and Alaska (United States); and to 2018 for Nunavut, N.W. Territories, and Yukon regions in Canada. Robust standard errors are clustered at the country level.
Source: OECD elaboration based on the OECD Region and Cities databases.
Productivity growth over the past decade complemented gains in labour market participation but not employment across OECD regions (Figure 1.16). There is a positive correlation between labour productivity growth and the change in labour force participation rates. For employment, this correlation is null. Indeed, productivity gains can lead to job losses, for example, in the case of labour-saving technological change. Furthermore, job losses can mechanically raise labour productivity if output does not fall at the same rate as the number of workers. Nonetheless, productivity gains did go hand-in-hand with gains in participation rates: over four-fifths (83%) of regions where participation rates increased also exhibited overall productivity growth at the same time. Furthermore, about two-thirds (64%) of OECD regions are situated in the upper right quadrant of both graphs, indicating positive labour productivity gains with increases in both employment and participation rates over the past ten years. Additionally, almost three-fourths (74%) of regions experienced positive labour productivity growth, with an increase in either employment or participation, but not both. Yet, in almost one in twelve (8%) regions, productivity gains did not accompany either employment or participation gains.
Correlation between labour productivity growth and employment (left) or participation growth (right), 2012 to 2022 or the closest available years
Note: The figure shows the ten-year change in the employment rate (left) or the participation rate (right) on the x-axis, and the ten-year compound growth rate in labour productivity on the y-axis using the years 2012 to 2022, or the closest available years, for OECD regions with available data. For labour productivity, the first year refers to 2013 for Chile and the last year refers to 2021 for New Zealand, Norway, Switzerland and the United Kingdom; to 2020 for Australia and Alaska (United States); and to 2018 for Nunavut, N.W. Territories, and Yukon regions in Canada. The dotted line represents the correlation, and the grey shaded area represents the 95% confidence intervals between the two measures. Each dot represents a TL-2 region. Outliers are not shown. The employment rate is defined as the number of working-age employed persons out of the working-age population, where the working-age is defined as 15-64 year-olds. The participation rate is defined as the number of working-age employed persons or persons looking for work out of the working-age population, where the working-age is defined as 15-64 year-olds.
Source: OECD calculations based on the OECD Regional databases.
Regional resilience involves the ability of a local economy to weather and recover from shocks through adaptative changes in economic structures and social arrangements (Martin and Sunley, 2014[41]). These shocks include economic recession, natural disasters, and structural changes accelerated by megatrends such as the green, digital and demographic transitions. Policies that support a dynamic business environment, diversified sectoral base and local skills development are key to regional labour market quality, while at the same time, to building resilience in the face of external shocks. For example, targeted economic support measures, like maintaining employer-employee relationships and short-term business support schemes, can aid recovery without compromising efficiency but must be balanced to promote flexible labour markets. Active labour market policies, such as job-seeker support and skills development programmes, enhance regional resilience by improving employability and cushioning against unemployment during economic crises (Vermeulen, 2022[42]). Lastly, supporting vulnerable groups through in-work benefits, partial unemployment aid, and skills subsidies helps to build more equitable resilience. This section considers several indicators covering skill levels and mismatch, the take-up of non-traditional work on the worker side, and sectoral diversification and the case of mass layoffs on the firm side, to comment on the resilience of regional labour markets.
High-skilled jobs dominate regional employment, as the share of middle-skilled jobs shrinks
A diversified skills base in the regional labour force can help to enhance the quality and resilience of regional labour markets. Skills diversity not only supports adaptability to changing economic conditions but also fosters innovation and productivity growth (Aghion and Howitt, 2008[43]; Acemoglu and Autor, 2011[44]). Effective skills development programmes, including vocational training and lifelong learning initiatives, play a pivotal role in equipping workers with the necessary competencies to thrive in evolving industries (European Commission, 2020[45]; Winthrop, Mcgivney and Fellow, 2016[46]). This is in addition to investments in high-quality education and training, which can enhance workforce flexibility and reduce vulnerability to economic shocks, thereby bolstering regional resilience (Heckman and Kautz, 2012[47]; World Bank Group, 2019[48]). Lastly, policies that promote skills matching, such as job placement services and apprenticeship programmes, facilitate smoother transitions for workers and contribute to overall labour market efficiency (European Commission, 2020[49]; Autor, 2014[50]; OECD, 2018[51]). By prioritising a diverse skills agenda, regions can better withstand disruptions and position themselves for sustained economic growth in an increasingly competitive global landscape.
Occupations that require a high level of skills account for the largest proportion of jobs in OECD regions. In more than half (55%) of OECD regions, most workers are employed in high-skilled jobs, followed by three in eleven (27%) regions where most workers are in medium-skilled jobs (Figure 1.17). This highlights a trend towards more advanced, professional, and technical occupations in the overall distribution of jobs within OECD regions. Yet, this distribution varies significantly across countries, and in some cases within countries, likely reflecting differences in education systems, labour market policies, and economic structures. Within-country differences tend to be driven by the capital-city region, where high-skilled jobs dominate. This is the case in Colombia, Greece, Korea, Mexico, and Portugal, where only the capital-city region has the high-skilled as the most common occupational skill level. In 5 out of 28 OECD countries with available data, there is at least one region where each of the skill levels dominates, reflecting significant within-country differences in the skill levels demanded by the labour market.
Note: The figure shows the most common job skill level and its share for OECD regions in 2023 or latest available year. For European Union countries, the data refers to 2022 and for Korea, to 2021. Job skill is defined using ISCO occupational categories. Low-skilled corresponds to jobs in sales and services and un-skilled occupations (ISCO 5 and 9), medium-skilled workers hold jobs as clerks, craft workers, plant and machine operators and assemblers (ISCO 4, 7 and 8), and high-skilled workers are those who have jobs in managerial, professional, technical and associated professional occupations (ISCO 1, 2 and 3). The definition of skill is based on the educational level thought to be required of an occupation and does not consider skills not related to educational level. The sample is all TL-2 regions with available data.
Source: OECD calculations based on national labour force surveys for the European Union (including the OECD accession countries of Bulgaria, Croatia and Romania), Canada, Chile, Colombia, Costa Rica, Korea, Mexico, the United States and the United Kingdom.
Over the past decade, the share of middle-skilled jobs contracted in OECD regions, as the share of high-skilled jobs increased. In four in five (80%) OECD regions with available data, the share of middle-skilled jobs fell over 2013 to 2023, falling significantly by over five percentage points in almost two in eleven (22%) regions (Figure 1.18). To a large extent, increasing demand for high-skilled jobs compensated for the falling share of middle-skilled jobs. The share of high-skilled jobs grew in three-fourths (75%) of regions where the share of middle-skilled jobs fell. In contrast, in a majority (63%) of OECD regions, the share of low-skilled jobs changed by less than three percentage points. In one in eleven (11%) regions, the share of low-skilled jobs grew by over three percentage points and in tandem with the share of high-skilled jobs. While in more than half (53%) of regions, low-skilled and middle-skilled jobs fell together, as high-skilled jobs grew. Similar to the skill distribution across countries, this trend indicates a shift towards more managerial, professional, technical, and associated professional occupations at the cost of a decline in clerks, craft workers, plant and machine operators and assemblers. This may reflect a shifting skills demand, driven by changes in technology, automation, and the global economic landscape. In contrast, there is a consistent demand for low-skilled jobs in sales, services and unskilled occupations, despite these advancements in technology and economic shifts.
Correlation between the ten-year change in the regional skill distribution, 2013 to 2023 or closest available years
Note: The figure shows the ten-year change in the share of high-skilled jobs on the x-axis and the ten-year change in the share of medium-skilled jobs on the y-axis. The colour of the point refers to the ten-year change in the share of low-skilled jobs. The ten-year period is 2013 to 2023, or closest available years. The time period refers to 2013 to 2022 for European Union countries, to 2015 to 2023 for the United Kingdom; and 2013 to 2021 for Korea. The dotted line represents the correlation line, and the grey shaded area represents the 95% confidence intervals between the two measures. The estimate of the correlation is listed on the top right of each graph with standard error in paratheses. Each dot represents a TL-2 region. Outliers, defined as regions with values in the top or bottom 5% of the distribution, are not included. Job skill is defined using ISCO occupational categories. Low-skilled corresponds to jobs in sales and services and un-skilled occupations (ISCO 5 and 9), medium-skilled workers hold jobs as clerks, craft workers, plant and machine operators and assemblers (ISCO 4, 7 and 8), and high-skilled workers are those who have jobs in managerial, professional, technical and associated professional occupations (ISCO 1, 2 and 3). The definition of skill is based on the educational level thought to be required of an occupation and does not consider skills not related to educational level. The sample is all TL-2 regions in OECD countries with available data.
Source: OECD calculations based on national labour force surveys for the European Union, Canada, Chile, Colombia, Costa Rica, Korea, Mexico, the United States and the United Kingdom.
Regional disparities in over- and under-skilling highlight challenges in labour market alignment
Skills mismatches, defined as discrepancies between the skills of workers and those demanded by employers, pose challenges for aligning labour market needs with available talent.3 These mismatches can result in underemployment—where individuals work in jobs that do not fully utilise their skills—, in in workers underqualified for the jobs they are employed in, or in job vacancies that remain unfilled due to a lack of qualified candidates. This friction can influence the economic performance of regions, weighing on growth and the capacity to respond to market changes effectively. As regions strive to enhance their economic resilience, understanding and addressing skill mismatches becomes increasingly important.
Skill mismatches are prevalent across OECD regions with almost one in three employed individuals working in jobs that do not match their skill level, regardless of whether they are over- or under-qualified (Figure 1.19). Countries such as Czechia, Lithuania, and Slovakia exhibit relatively low regional median skill mismatches, around 16.5% to 23%. Conversely, the top four OECD countries experiencing the greatest degree of mismatch, are Korea (41.8%), Costa Rica (41.1%), Colombia (40.5%), and the United Kingdom (40%). Low mismatch suggests effective alignment in most regions, given equally low regional dispersion of around 7.5 to 9 percentage points difference between the region with the highest and lowest mismatch. Conversely, 10 out of the 33 OECD countries with available data indicate large regional dispersion with a difference of over ten percentage points. The largest dispersions are present in high mismatch countries, such as Korea, Mexico and Colombia, where the difference is over 30 percentage points, 20 percentage points and over 15 percentage points, respectively. The difference between the region with the highest and lowest mismatch is also large in the United States (21 percentage points), despite having a lower-than-average regional mismatch.
Note: The figure shows the regional dispersion (highest, lowest and median value) in the share of workers in mismatched jobs in 2023 or the latest available year. For European Union countries, the data refers to 2022, and for Korea, to 2021. Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECD's Directorate for Employment, Labour and Social Affairs, whereby a worker is in a mismatched job when their educational skill level does not match the most common skill level of workers in that occupational group in that country. The sample is all TL-2 regions with available data.
Source: OECD calculations based on national labour force surveys for the European Union (including the OECD accession countries of Bulgaria, Croatia and Romania), Canada, Chile, Colombia, Costa Rica, Korea, Mexico, the United States and the United Kingdom.
Capital-city regions, ageing regions and regions with a higher share of green jobs saw the share of mismatched workers fall over the past ten years (Figure 1.20). Ageing regions, or regions where the old-age dependency ratio increased over the past five years, are correlated with a 3.2 percentage point decrease in the share of mismatch. This is likely driven by both the exit of older workers with less education and an overall demand shift toward a more educated labour force. Job mismatch also fell by 1.4 percentage points in capital-city regions, and marginally by 0.2 percentage points in regions with a higher share of green jobs, compared to regions in the same country. The share of mismatched workers is lower in capital-city regions, compared to other regions in the same country (Annex Figure 1.B.6). In contrast, other demographic (such as an ageing population), or economic (such as the sectoral employment composition) characteristic is not correlated with within-country differences in the share of mismatch.
Within-country correlation of the ten-year change in the share of mismatch (pp) to selected characteristics, 2023 or latest available year
Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. The graph shows the coefficient and 90% confidence intervals of separate multivariate regressions of the ten-year change in the share of job mismatch from 2013 to 2023 (or closest available years) on a dummy for capital-city regions, ageing regions (defined as those that experienced an increase in the elder-dependency rate over the past five years), for an above national median employment share in green jobs in 2021, in tradeable services (ISIC broad sectors G to N), tradeable goods (ISIC sectors B, D, E) or neither tradeable goods nor services. The coefficient (`within-country correlation') presents the within-county percentage point difference in the share of mismatch based on the characteristic on the x-axis. Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECD's Directorate for Employment, Labour and Social Affairs, whereby a worker is in a mismatched job when their educational skill level does not match the most common educational skill level of workers in that occupational group in that country. Each regression also controls for the log of population in 2023 or latest available year and country fixed effects. For European Union countries, the data refers to 2013 to 2022, for Korea, to 2013 to 2021, and for the United Kingdom, to 2015 to 2023. The level of observation is the TL-2 region. The sample of countries includes all OECD countries. Robust standard errors are clustered at the country level.
Source: OECD elaboration based on the OECD Regional databases.
Regions with a higher prevalence of over-skilled workers generally exhibit lower levels of under-skilling (Figure 1.21). The mean difference between the share of underqualified and overqualified workers is around nine percentage points across OECD regions. The top five regions with the greatest difference are all in Canada, with over 30% of over-skilled workers and 1.5% to 2.3% of under-skilled workers. In contrast, the regions with the smallest differences between the share of over-skilled and under-skilled workers are in three different countries (Italy, Poland, Spain and the United States), with a difference of under 0.3 percentage points. Regions with a high share of either over- or under-skilled workers may be experiencing a high- or low-skill equilibrium, whereby the skills demand of jobs adapts to match the skills supply of the population. This can be a particular issue in a low-skill equilibrium when employers adopt a price-based competition strategy that relies on low-skilled and standardised production (OECD, 2014[52]). Jeju region (Korea) stands out with a high share of both under- and over-skilled workers at over 30% each, likely due to its economy specialised in agriculture, fishing and tourism, which differs from the rest of Korea, which is more industrialised. As such, workers in the same occupation in Jeju likely require different skills than their counterparts in the rest of Korea. The regional economic structure, industrial composition and educational system are likely to all contribute to this distinct distribution of skill mismatches.
Note: The figure shows the share of over- and under-skilled workers for each OECD TL-2 region in 2023 or the latest available year. For European Union countries, the data refers to 2022, and for Korea, to 2021. Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECD's Directorate for Employment, Labour and Social Affairs, whereby a worker is in a mismatched job when their educational skill level does not match the most common educational skill level of workers in that occupational group in that country. `Over-skilled' means that the worker has an educational skill level above the most common educational skill level of their occupation. `Under-skilled' means that the worker has an educational skill level below the most common educational skill level of their occupation.
Source: OECD calculations based on national labour force surveys for the OECD countries in the European Union, Canada, Chile, Colombia, Costa Rica, Korea, Mexico, the United States and the United Kingdom.
Spatial variation in the incidence of over- and under-skill across regions highlights distinct patterns in how educational qualifications align with labour market demands. There exist opportunities in some countries to leverage complementarities between types of skill mismatch. For example, Illinois (United States) has 18% of under-skilled workers, while its neighbouring regions, such as Indiana and Wisconsin, have 16% of over-skilled workers (Figure 1.22). Overall, for half of OECD regions with available data, there exists a region in the same country with a complementary type of mismatch. In contrast, in 10 out of the 33 OECD countries with available data, all regions display the same type of mismatch, whether it is over-skilling or under-skilling. In these countries, skill mismatch is likely driven by national trends. Vocational education and training systems (VET) can be leveraged to bridge skill gaps by providing skills training in alignment with industry needs. At the same time, these systems should be flexible with recognised credentials, to respond to evolving skills needs and labour market transitions such as in Germany, Austria and Switzerland (OECD, 2023[53]).
Note: The map shows the most common type of mismatch, workers in jobs below their skill level (“under-skilling”) or workers in jobs above their skill level (“over-skilling”) in 2023 or latest available year. For European Union countries, the data refers to 2022, and for Korea, to 2021. Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECD's Directorate for Employment, Labour and Social Affairs, whereby a worker is in a mismatched job when their educational skill level does not match the most common skill level of workers in that occupational group in that country. The sample is all TL-2 regions with available data.
Source: OECD calculations based on national labour force surveys for the European Union (including the OECD accession countries of Bulgaria, Croatia and Romania), Canada, Chile, Colombia, Costa Rica, Korea, Mexico, the United States and the United Kingdom.
Across regions, self-employment is higher where more traditional jobs are lacking
Non-traditional forms of employment, such as self-employment, as well as part-time and temporary work, play an important role in today's labour markets, reflecting broader shifts in work arrangements across OECD countries. Self-employment, defined by individuals owning and operating their businesses, provides independence and can encourage labour market engagement among those desiring more control over their work, exploring entrepreneurial ventures, or adapting to unique personal circumstances. Part-time employment, characterised by fewer working hours per week than full-time jobs, provides flexibility and can facilitate increased labour market participation among students, caregivers, and older adults. Similarly, temporary work, including fixed-term contracts and seasonal employment, offers both employers and employees greater adaptability in response to fluctuating economic conditions and personal circumstances. Take-up of the latter two forms of non-traditional forms of work is mainly driven by structural national policies, implying few regional dimensions. See Annex Figure 1.B.8 to Annex Figure 1.B.11 in Annex 1.B for more information about the regional take-up of part-time and temporary work.
Non-traditional work arrangements also present challenges, such as lower job security, reduced career progression opportunities, and often limited access to benefits compared to permanent, full-time roles (OECD, 2018[54]; OECD, 2019[55]). In particular, self-employment can raise specific challenges, such as income instability, difficulty in accessing credit and financing, and the burden of managing administrative tasks and regulatory compliance (OECD, 2019[55]; OECD/European Union, 2017[56]). Rather than a deliberate choice for greater work autonomy, workers may engage in self-employed work due to a lack of other options. In addition, it tends to be under-represented among women, youth, the elderly, immigrants, and the unemployed (OECD/European Commission, 2023[57]).4 Consequently, understanding the dynamics in the take-up of non-traditional employment allows for the development of comprehensive policies that promote equitable work conditions and protection for all workers, regardless of their employment status.
Median within-country regional dispersion in self-employment rates stands at over 6 percentage points (Figure 1.23). Yet, this difference between the regions with the most and least self-employed ranges from about 0.5 percentage points in Austria to over 21 percentage points in Greece. Countries with a high overall share of self-employed tend to also have high regional dispersion. For example, in Greece, Italy and Poland, the median share of self-employed across all regions is over 18%, with regional dispersion at over eight percentage points. France is an exception, as the overall share of self-employed resembles the OECD average, yet it has high regional dispersion at over 17 percentage points. In contrast, regional self-employment rates are limited in Austria, Norway and Denmark, where the difference between the region with the highest and lowest share of the self-employed is under two percentage points. Regions, where the tourism and agriculture sectors dominate, such as in Peloponnese (Greece), Podlaskie (Poland), Corsica (France), and Molise (Italy), tend to have the highest self-employment rates.
Note: The figure shows the regional dispersion (highest, lowest and median value) in the share of self-employed among all working-age employed persons in 2022. The working-age population is defined as 15-64 year olds. The sample is all TL-2 regions with available data.
Source: OECD calculations based on national labour force surveys for the European Union (including the OECD accession countries of Bulgaria, Croatia and Romania).
Regional disparities in the rates of self-employment between regions with the highest and lowest rates increased by three percentage points following the COVID-19 shock (Figure 1.24). In 2013, high self-employment regions had rates of self-employment almost 47% higher than regions with low-self-employment, in the same country. By 2022, this ratio rose to almost 50% higher. The difference between the top and bottom quintile of regions, based on self-employment rates, increased from about 33% of the national median to almost 35%. The increase is mostly driven by falling self-employment rates in regions in the bottom quintile of self-employment, relative to the national median.
Evolution of the self-employment rate relative to the national median, 2013 to 2022
Note: The figure shows the evolution of the share of self-employed among the employed in the working age population (15-64 year-olds), relative to the national median (which corresponds to 100 on the top graph), for the top and bottom 20% of regions which account for at least 20% of the population. The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period.
Source: OECD calculations based on national labour force surveys for the OECD countries in the European Union.
Self-employment is marginally most prevalent in regions facing higher unemployment rates (Figure 1.25). In regions with unemployment rates below the national median, about 17% of workers are self-employed at the start of the period. For regions facing unemployment rates above the national median, the self-employed represented almost 18% of workers. The difference in the share of self-employed between higher and lower unemployment regions, in the same country, remained relatively stable at about 0.5 percentage points, up until 2020. In 2021, the trend briefly reversed so that regions with unemployment rates below the national median had marginally higher rates of self-employment. By 2022, the difference in the share of self-employed for regions above and below the national median narrowed to about 0.1 percentage points. This may be linked to overall falling unemployment rates, especially in high-unemployment regions (Annex Figure 1.B.7). High unemployment rates suggest that jobs are scarce so more traditional types of work are difficult to find. Thus, rather than solely representing a voluntary shift to a more autonomous working environment, self-employment take-up is also likely a recourse from a difficult labour market situation.
Evolution of the self-employment rate given initial unemployment rate, 2013 to 2022
Note: The figure shows the evolution of the share of self-employed among the employed in the working-age population for the regions above and below the national-specific median of unemployment rates in 2013. The unemployment rate is defined as the share of persons looking for work as a percentage of the labour force (employed or looking for work) in the working-age population (15-64 year-olds). The sample is all regions in OECD countries with data available over the entire time period.
Source: OECD calculations based on national labour force surveys for the OECD countries in the European Union.
Sectoral diversity and mass layoffs: The role of firms in regional resilience
The adaptability and competitiveness of firms are pivotal in determining a region’s capacity to withstand economic shocks and capitalise on new opportunities for innovation, job creation and productivity gains. As regions navigate the challenges posed by globalisation, technological advancements, and changing market demands, the strength and diversity of local businesses and sectors become instrumental in promoting sustainable development and long-term economic stability. Therefore, a deep understanding of the firm and sectoral structure of regions is essential for policymakers to design informed and effective strategies that enhance regional resilience.
The composition of sectoral employment represents an important feature of regional economies with implications for economic diversity and resilience. Regions dominated by a limited number of industries often face increased vulnerability to economic fluctuations and external shocks, such as technological changes or global market shifts, even if regions concentrated in trade-exposed sectors tend to be relatively more resistant (OECD, 2018[19]). Conversely, regions with a diversified employment base tend to exhibit greater economic stability and adaptability (Audretsch and Feldman, 1996[58]; Giannakis, Bruggeman and Mamuneas, 2024[59]; Delgado, Porter and Stern, 2014[60]). This sectoral diversification also influences labour market dynamics, affecting everything from wage levels to employment opportunities and workforce skills development (Autor, Katz and Kearney, 2008[61]; Boeri et al., 2019[62]; Greenstone, Hornbeck and Moretti, 2010[63]). It is important to understand these dynamics to mitigate the risk associated with economic specialisation.
In over two-thirds of OECD regions, the sectoral composition of employment shows moderately-low or low diversification (Figure 1.26). Diversification is measured using the Herfindahl-Hirschman Index (HHI) (See Box 1.3 for details). This is driven mainly by a high share of regions with moderately-low diversified labour markets: only 13% of OECD regions with available data (57 out of 419 regions) display a low sectoral diversification of employment (mean HHI index of 2 822), while in ten regions, sectoral employment diversification is especially low (over 3 000). While the region with the highest index score (and lowest diversification) is Nunavut in Canada (likely due to its remoteness and low population density), other top regions include three regions in Czechia, two in Mexico and Türkiye, and one in Romania and Greece. Then, 142 out of 419 regions (34%) are in moderately-high diversified labour markets. In contrast, no regions fall into the category of high diversification, likely due to the low total number of available sectors. The five regions with the highest diversification are Luxembourg, Central (Costa Rica), Prague (Czechia), Warsaw (Poland), and Tel Aviv (Israel) (HHI ranging from 1 615 to 1 661). ‘
The Herfindahl-Hirschman Index (HHI) is a standard measure of market concentration, usually used to consider firm power in a certain product market, sector or economy (Antitrust Division, 2024[64]). When considering labour markets, this measure can be adapted to look at firms’ share of vacancies, new hires, or employment within a market (OECD, 2022[7]). To consider sectoral diversification, this chapter takes the share of workers employed in each sector in a subnational region.
The measure is thus defined as the sum of the squared percentage shares of each sector in the economy. In this way, the index accounts for the relative size distribution of firms in the regional economy. Given data availability, sectors are classified into ten broad categories: "Agriculture, Forestry & Fishing", "Industry", "Manufacturing", "Construction", "Trade, Repair, Transport, Accommodation", "Information and communication", "Financial and insurance activities", "Real estate activities", "Professional, scientific and technical activities; administrative and support service activities", "Public admin., Defence, Edu., Health, Social", and "Arts, Entertainment, Recreation". It ranges from 1 000 when a labour market is occupied by an equal share of employment in each of the ten sectors, and a maximum of 10 000 if all employment is concentrated in one sector.
Given the high level of aggregation of the sectors considered, the measure presented in this chapter should not be taken to be an indicator of the concentration of a regional labour market. Instead, the purpose is to give a sense of the sectoral diversity of the distribution of employment and to comment on sub-regional differences.
Conventionally, a market is considered concentrated (or less diverse) if it has an HHI of 2 500 or above, a threshold usually considered conservative (Nocke and Whinston, 2022[65]; Affeldt et al., 2021[66]). A moderately concentrated (or moderately less diverse) market is between 1 500 and 2 500, which can be further broken down into moderately-low (HHI of 1 500 to 2 000), moderately-high (HHI of 2 000 to 2 500) and a low concentrated market is an HHI below 1 500 (US Department of Justice and Federal Trade Commission, 2010[67]).
Recent work found pervasive labour market concentration in OECD countries using harmonised data on job postings: 16% of business-sector workers in 15 OECD countries are in national labour markets that are at least moderately concentrated and 10% in highly concentrated markets. Exploiting harmonised linked employer-employee databases, it also finds evidence of monopsony power, i.e. firm discretion in setting wages and working conditions in contrast to competitive markets where firms must pay workers the “market rate” aligned with their productivity: 10% of workers employed in the most concentrated labour markets experience a wage penalty of at least 5% compared to a worker in a median-concentrated market. Job quality is also affected, as evident in the increased use of flexible and temporary contracts, as well as higher skill requirements (OECD, 2022[7]). Reintroducing competition into labour markets requires policies that increase the relative bargaining power of workers and promote fair wage-setting mechanisms in the face of power imbalances, towards the goal of improved labour market efficiency.
Note: The map shows the regional distribution of the degree of diversification of employment in 2023 or the last available year. The data refers to 2022 for Belgium, Colombia, Czechia, Denmark, Estonia, France, Hungary, Luxembourg, Malta, Mexico, Slovenia and Spain; to 2021 for Austria, Bulgaria, Croatia, Finland, Germany, Greece, Ireland, Italy, Japan, Latvia, Lithuania, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Sweden, Switzerland, and the United Kingdom; to 2020 for the United States; and to 2014 for Iceland. The colour represents the value of the employment diversification index, measured through the Herfindahl-Hirschman Index, which is the sum of the squared shares of employment of ten broad industries: "Agriculture, Forestry & Fishing", "Industry", "Manufacturing", "Construction", "Trade, Repair, Transport, Accommodation", "Information and communication", "Financial and insurance activities", "Real estate activities", "Professional, scientific and technical activities; administrative and support service activities", "Public admin., Defence, Edu., Health, Social", and "Arts, Entertainment, Recreation". High diversification is values below 1 500, moderate-high diversification is values between 1 500 and 2 000, moderate-low diversification is values between 2 000 and 2 500, and values above 2 500 represent a low degree of diversification. The thresholds are adapted from the definition of concentration by the U.S. Department of Justice and Federal Trade Commission (US Department of Justice and Federal Trade Commission, 2010[67]). The sample is all TL-2 regions (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data.
Source: OECD calculations based on the OECD Regional databases.
Industry is the dominant sector (i.e. the sector with the highest employment share) in the least diversified labour markets, leading in 24 regions. “Trade, Repair, Transport and Accommodation”, “Public services” and “Agriculture” are the dominant sector in 12, 13, and 8 regions, respectively. Industry is the most common sector due to the complementary between the “Industry” sector and the “Manufacturing” sector. In almost 92% (22 out of 24) of regions with low diversification driven by the “Industry” sector, “Manufacturing” represents the second highest sectoral share of employment. The findings highlight interdependencies between these sectors which drive economic specialisation of the region, but also potentially limiting the scope for further diversification.
Note: The figure shows the share of regions in each country that belong to each category which represents the number of sectors that cumulatively represent a majority (50% or higher) of employment in that region for the year 2023 or the latest available year. The data refers to 2022 for Belgium, Colombia, Czechia, Denmark, Estonia, France, Hungary, Luxembourg, Malta, Mexico, Slovenia and Spain; to 2021 for Austria, Bulgaria, Croatia, Finland, Germany, Greece, Ireland, Italy, Japan, Latvia, Lithuania, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Sweden, Switzerland, and the United Kingdom; to 2020 for the United States; and to 2014 for Iceland. The sectors considered are ten broad groups based on NACE-REV2 categories: "Agriculture, Forestry & Fishing", "Industry", "Manufacturing", "Construction", "Trade, Repair, Transport, Accommodation", "Information and communication", "Financial and insurance activities", "Real estate activities", "Professional, scientific and technical activities; administrative and support service activities", "Public admin., Defence, Edu., Health, Social", and "Arts, Entertainment, Recreation". The sample is all TL-2 regions (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data.
Source: OECD calculations based on the OECD Regional databases.
Two sectors or fewer are responsible for employing at least half of all workers in the region in almost two-thirds of OECD regions with available data (Figure 1.27). In three OECD regions, one industry accounts for more than half of employment, indicating a high level of specialisation in that sector. In contrast, a majority of employment is spread across three sectors in all regions of Estonia, Latvia, Lithuania, Luxembourg, and Switzerland. This is the case for only two sectors in all regions of Australia, Denmark, Iceland, Japan, the United Kingdom and the United States. Thus, in many OECD regions, few industries account for the majority of employment, highlighting the limited degree of economic diversification across OECD regions.
Industry-specific downturns can contribute to mass layoffs, which can destabilise regional economies given the relative size of the shock. Mass layoffs are defined as the separation of a significant number of employees from a single establishment over a short period. These events often occur for many reasons, for example, in response to economic downturns, industry restructuring, or technological advancements (Silva et al., 2019[68]; Chhinzer, 2023[69]). The occurrence of mass layoffs can significantly disrupt regional labour markets, leading to sudden increases in unemployment, persistent wage losses, reductions in consumer spending, and broader economic downturns within affected regions (Vermeulen and Braakmann, 2023[70]; Foote, Grosz and Stevens, 2018[71]; Cederlöf, 2021[72]; Flaaen, Shapiro and Sorkin, 2017[73]; Arquié and Grjebine, 2024[74]). For example, job losses are recorded not only in the directly affected establishments but also in nearby businesses and regions, indicating sizeable spillover effects on regional economies (Gathmann, Helm and Schönberg, 2018[75]). Thus, while the direct consequences are often severe, leading to substantial job losses, the indirect effects can also profoundly alter the economic stability of the affected regions. This is particularly true for regional labour markets as these events affect a larger proportion of workers than at the national level.
Mass layoffs are prevalent among OECD regions although there is considerable range in their occurrence over the past decade. Figure 1.28 presents the spatial variation in the instance of mass layoffs over the past ten years in European regions.5 In almost one-tenth (9%) of OECD regions in the European Union with available data, no mass layoffs are reported, while in the same proportion of regions, over 50 mass layoff events occurred. Five regions experienced one hundred or more of these events, with the most mass layoffs occurring in North Rhine-Westphalia (Germany) with over 130 events. Despite this, the majority of regions with available data experienced a modest number of mass layoffs in the past ten years: two in five (41%) regions had less than 10 mass layoff events, and almost three in five (60%), had less than 15 mass layoffs. There is a noted spatial dimension to the occurrence of mass layoff events linked to the bias in the data towards medium and large firms, especially in the manufacturing sector. Mass layoffs are notably most prevalent in current or historical industrial and manufacturing hubs, such as central Germany, northern United Kingdom, eastern France, and others. Lastly, apart from a brief increase during the COVID-19 pandemic, the frequency of mass layoffs has remained relatively constant over the past decade.
Note: The map presents the number of mass layoffs that occurred in the region over the past 10 years. A mass layoff is defined as the announced destruction of (1) at least 100 jobs or (2) affects at least 10% of the workforce at sites (i.e. workplaces) employing more than 250 people. This means that the lower bound of job loss for a mass layoff event is at least 25 workers. The information on mass layoffs comes from a database of large restructuring events reported in the principal national media and company websites, collected by Eurofound. Importantly, this database is not representative of mass layoff events as the size requirement leads to a bias towards medium and large firms, especially in the manufacturing sector. The sample is TL-2 regions in OECD countries with available data.
Source: OECD calculations based on the European Restructuring Monitor (Eurofound, 2024[76]).
This chapter provides an analysis of the current state and evolution as well as the resilience of regional labour markets in the aftermath of major shocks, reflecting on the recovery process and the capacity of regions to handle significant transitions. It assesses recovery from the COVID-19 pandemic, implications for employment and productivity, and explores various indicators that highlight the adaptability of regional labour markets to leverage the full potential of both workers and firms.
While employment rates across OECD regions have reached record highs, the decade-long view reveals persistent regional disparities in employment and participation rates. However, the recovery has come at the cost of labour market exclusion for specific groups, such as young workers, who have experienced widening age disparities. This trend will likely aggravate the current context of tight labour markets where firms report difficulty in recruitment. Chapter 2 examines the issue of labour market shortages by zooming in on the places, sectors and occupations facing greater difficulties in finding workers. Identifying these trends allows for the design of well-informed policy to address the bottleneck caused by labour market shortages and its impact on employment and local development.
Labour productivity growth has been lagging in many OECD regions, with little progress in closing the gap between the top and bottom performers. Notably, in half of OECD regions, labour productivity growth over the past decade was below 0.8% per year, indicating that gains in labour market outcomes have not necessarily been accompanied by proportional productivity increases. Population ageing contributes to declines in per capita income and limits productivity growth, but it may also incentivise the adoption of labour-enhancing or labour-saving technologies like AI, depending on its efficiency in job-specific tasks (Nedelkoska and Quintini, 2018[5]; André, Gal and Schief, 2024[77]). Chapter 3, through new estimates on occupational exposure to AI, discusses the double-edged sword of the integration of AI in the workforce. In order for AI to support rather than undermine productivity growth, policymakers may want to consider the pace and nature of AI adoption, the ability of workers to adapt, and the need for transitional support policies.
A flexible and diversified skills base in the regional labour force contributes to the increased quality and resilience of regional labour markets. However, skill mismatches are prevalent across OECD regions, and non-traditional forms of employment, such as self-employment, play an important role in today's labour markets. Furthermore, in over two-thirds of OECD regions, labour markets exhibit moderately low or low employment diversification. Amid accelerating green, digital, and demographic transitions, policy measures that contribute to building resilience through labour markets that encourage flexibility and integrate regional labour market demands to establish realistic career pathways across different regions are needed.
In conclusion, the diversity of regional experiences across OECD countries highlights both achievements and ongoing challenges. Valuable lessons can be drawn from these experiences, including the importance of enhancing the quality and diversity of the regional labour force, effectively managing sectoral employment diversification and potential vulnerabilities like mass layoffs, leveraging technological advancements, such as artificial intelligence, to address lagging productivity growth, and prioritising skills and job development. In light of past trends and in anticipation of future challenges, it is essential to adopt proactive place-based strategies that foster more inclusive and resilient labour markets in the future.
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[9] OECD (2022), “How vulnerable is European manufacturing to gas supply conditions?: A regional approach”, OECD Policy Responses on the Impacts of the War in Ukraine, OECD Publishing, Paris, https://doi.org/10.1787/01278ba3-en.
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Youth
The EU Youth Guarantee was reinforced by member states in 2020 by committing that people under 30 receive quality employment, continued education, or a traineeship within four months of becoming unemployed or leaving education (European Commission, 2020[78]). Since its establishment in 2013, the European Union improved and expanded employment services for youth such as continued education, traineeship or connecting them with employment opportunities, resulting in a record-low drop in youth unemployment of 14.9% in 2020 and 1.7 million fewer youth in neither employment, education or training (NEETs) (European Commission, 2020[78]). The reinforced programme has a broader target group of 15-to 29-year-olds at risk of unemployment or unable to enter the labour market and offers individualised approaches; providing youth with the appropriate guidance and helping them find courses or boot camps for upskilling (European Commission, 2020[78]).
The Municipality of Rotterdam, in the Netherlands gave training vouchers to residents facing economic and labour market challenges due to the pandemic. The vouchers paid for courses and training in professional areas in high demand, to adjust to the changes produced by the pandemic. The programme was successful in improving the labour market and continued beyond the pandemic as an active and integral part of the youth labour market activation. The municipality will expand the programme to over 20 000 vouchers by 2024 (OECD, 2023[79]).
Women
Canada promoted a CAD 10-a-day child care framework, which was implemented through bilateral agreements with provinces and territories. The objective is to get more women into the labour force. Since this investment in the federal budget of 2021 and the culmination of the regional agreement negotiation, labour force participation among working age mothers with young children is at a record high of 79.7% (Employment and Social Development Canada, 2024[80]). The government is now making more progress by increasing inclusive access to child care, with funding focused on underserved communities (Employment and Social Development Canada, 2024[80]).
Donegal, in Ireland implemented the Women’s Integration Skills and Employment Project (WISE), which provides services to support women entering and re-entering the labour market. Over 70% of women in WISE have found a job and sustained employment in the long-term (OECD, 2022[81]). WISE worked by assigning participants to a personal advisor who supports training in writing CVs, improving interview skills, and understanding employment contracts. In addition, it matched women with local employer opportunities and assisted with access to public funding for training and childcare (European Commission, 2019[82]).
Elderly
In Kamikatsu, Japan, more than half of the 1 415 population is 65 and older. The waning agriculture industry resulted in economic decline and depopulation. In response, the public-private Irodori Corporation brought together local farmers and the town government to sell tsumamono, a leaf used in Japanese gastronomy. Almost 200 farmers are aged 70 on average, 90% being women (OECD, 2024[83]). The programme impacted well-being as the region has the lowest per capita costs of medical care in the prefecture, despite having a large proportion of older individuals (OECD, 2024[83]).
Suwon, in South Korea, established nearly 600 lifelong learning centres to help residents improve career prospects and participate in adult learning (OECD, 2020[84]). The initiative aims to support disadvantaged groups, such as the elderly, by increasing access to learning spaces. The city also offers subsidies for citizen-developed learning programmes, involves NGOs in running lifelong learning initiatives, and supports the certification of lifelong learning teachers (OECD, 2020[84]). This certification requires mandatory training and has been introduced by the Government of Korea, for people who want to become lifelong learning teachers (UNESCO, 2023[85]). This bottom-up approach to adult learning helps the elderly to actively engage in society, offering opportunities to continue learning and contributing (OECD, 2020[84]).
Within-country correlation of ten-year employment change (pp) to selected characteristics, 2013 to 2023 or closest available years
Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. The graph shows the coefficient and 90% confidence intervals of separate multivariate regressions of the ten-year change in employment rates (2013 to 2023) on a dummy for capital-city regions, ageing regions (defined as those that experienced an increase in the elder-dependency rate over the past five years), for an above national median employment share in green jobs in 2021, in tradeable services (ISIC broad sectors G to N), tradeable goods (ISIC sectors B, D, E) or neither tradeable goods nor services. Each regression also controls for the log of population in 2023, or latest available year, and country fixed effects. The level of observation is the TL-2 regions. The sample of countries includes all OECD countries. Robust standard errors are clustered at the country level.
Source: OECD elaboration based on the OECD Region and Cities databases.
Average participation rate of OECD regions by gender and age and their differences, 2013 to 2023
Note: The figure shows the mean participation rate for OECD regions in the years 2013 to 2023, by demographic group. The mean participation rate for men and women is presented in the first panel, for workers aged 15 to 24 and workers aged 25 to 64 in the second panel, and the difference in the participation rate between men and women and older and younger workers in the bottom panel.
Source: OECD calculations based on the OECD Regional databases.
Note: The figure shows the regional dispersion (highest, lowest and median value) in the NEET rate (not in employment, education or training) for the youth working-age population (15-24 year-olds) in 2023 or latest available year. The last year refers to 2022 for Australia, Belgium, Colombia, Israel, Japan, Mexico, Slovak Republic (except for Bratislava where the data refers to 2017), Spain, Switzerland, the United Kingdom and the United States; to 2021 for Sweden; to 2019 for Bulgaria, Croatia, Denmark, France, Luxembourg, Malta, the Netherlands (except for Zealand where the data refers to 2016), and Romania; to 2017 for Chile; and to 2016 for Norway. The sample is all TL-2 regions in countries with available data, including the OECD accession countries of Bulgaria, Croatia and Romania.
Source: OECD calculations based on the OECD Regional databases.
Evolution of the NEET rate relative to the national median, 2013 to 2023
Note: The figure shows the evolution of the NEET rate (not in employment, education or training) for the youth working-age population (15-24 year-olds), relative to the national median (which corresponds to 100 on the top graph), for the top and bottom 20% of regions which account for at least 20% of the population in a country. The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period
Source: OECD calculations based on the OECD Regional databases.
Within-country correlation of ten-year annual labour productivity growth (pp) to selected characteristics, 2012 to 2022 or closest available years
Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. The graph shows the coefficient and 90% confidence intervals of separate multivariate regressions of annual labour productivity growth in the latest available year on a dummy for capital-city regions (large regions that include the capital city), regions with an above national median employment share in green jobs in 2021, in tradeable services (ISIC broad sectors G to N), tradeable goods (ISIC sectors B, D, E) or neither tradeable goods nor services. The coefficient (`within-country correlation') presents the within-country percentage point difference in productivity growth rates based on the characteristic on the x-axis. Each regression also controls for the log of population in the latest available year, a dummy for the latest available year, and country fixed effects. The level of observation is the TL-2 region. The sample of countries includes all OECD countries with available data, excluding Ireland. Robust standard errors are clustered at the country level.
Source: OECD elaboration based on the OECD Region and Cities databases.
Within-country correlation of the share of mismatch to selected characteristics, 2023 or latest available year
Note: ***p-value<0.01, **p-value<0.05, *p-value<0.1. The graph shows the coefficient and 90% confidence intervals of separate multivariate regressions of the share of job mismatch in 2023 (or latest available year) on a dummy for capital-city regions, ageing regions (defined as those that experienced an increase in the elder-dependency rate over the past five years), for an above national median employment share in green jobs in 2021, in tradeable services (ISIC broad sectors G to N), tradeable goods (ISIC sectors B, D, E) or neither tradeable goods nor services. The coefficient (`within-country correlation') presents the within-county percentage point difference in the share of mismatch based on the characteristic on the x-axis. Skill mismatch is calculated following the methodology of the Skills for Jobs Indicators of the OECD's Directorate for Employment, Labour and Social Affairs, whereby a worker is in a mismatched job when their educational skill level does not match the most common educational skill level of workers in that occupational group in that country. Each regression also controls for the log of population in 2023 or latest available year and country fixed effects. For European Union countries, the data refers to 2022, and for Korea, to 2021. The level of observation is the TL-2 region. The sample of countries includes all OECD countries. Robust standard errors are clustered at the country level.
Source: OECD elaboration based on the OECD Regional databases.
Note: The figure shows the evolution of the unemployment rate for the working-age population (15-64 year-olds) for the top and bottom 20% of regions in a country, which account for at least 20% of the population. The sample is all TL-2 regions in OECD countries with at least five regions and with data available over the entire period.
Source: OECD calculations based on the OECD Regional databases.
Note: The figure shows the regional dispersion (highest, lowest and median value) in the part-time employment rate for employed individuals, 15-64 year-olds in 2023 or the latest available year. The data refers to 2022 for Finland and Iceland; and to 2021 for Romania. When the region name is double-hyphenated, it signifies two regions with the same value. The sample is all TL-2 regions (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data.
Source: OECD calculations based on the OECD Regional databases.
On average across OECD regions, take-up of part-time work is higher than the OECD average, representing around 16% of employment versus 14.7% for the OECD as a whole (Annex Figure 1.B.8) (OECD, 2024[86]). Within-country variance is low. The mean difference between the region with the largest vs. lowest take-up of part-time work stands at around 4 percentage points. The exceptions are Türkiye and Australia, where this difference is around 17 and 12 percentage points, respectively. However, there is large variance in the take-up of part-time work across OECD countries and regions. The regional median region of the share of employment in part-time contracts varies widely from under 2% in Bulgaria, with the lowest regional share in North East and South Central at around 1% of employment, to over 42% in the Netherlands, with close to one in two workers engaged in part-time work in Groningen.
Yet, this aggregate picture conceals that women are almost 15 percentage points more likely to be employed in part-time contracts than men across OECD countries (Annex Figure 1.B.9). The gender difference in part-time work ranges from -0.55 percentage points in Romania to over 42 percentage points in the Netherlands and Switzerland, largely driven by high overall take-up of part-time work among women at over 60% in both countries. The ratio of women to men employed in part-time work is above two in all but six out of the thirty-one OECD countries with available data, reaching over 4 women for each man in Germany, Italy and Austria.
Note: The figure shows the gender distribution and difference in the regional median for each country for part-time employment rate for employed individuals, 15-64 year-olds in 2023 or the latest available year. The data refers to 2022 for Finland and Iceland; and to 2021 for Romania. The sample is all TL-2 regions (including the OECD accession countries of Bulgaria, Croatia and Romania) with available data.
Source: OECD calculations based on the OECD Regional databases.
The instance of temporary employment is slightly below part-time employment across OECD regions: about 12% of employed individuals are employed through fixed-term contracts (Annex Figure 1.B.10). Regional dispersion is also limited: the mean country difference between minimum and maximum regions in a country stands at about 4.5 percentage points, once the three countries with the greatest difference are excluded: Colombia (38.8 percentage points), Greece (21.2 percentage points) and Chile (18.2 percentage points). La Guajira in Colombia leads in the use of temporary work contracts, with over half of its workforce employed through these fixed-term arrangements. Following significantly behind is Aysén in Chile, where the share of employment in fixed-term contracts stands at about 37%—over 20 percentage points lower than La Guajira. In contrast, the lowest use of temporary contracts, less than 1%, is in the Central region of Costa Rica. In Estonia, Costa Rica, Australia, Slovak Republic, and Hungary, the take-up of temporary work is particularly low: less than 5% of workers are employed by these contracts.
Note: The figure shows the regional dispersion (highest, lowest and median value) in the share of employment on fixed-term contracts (15-64 year-olds) in 2023 or the latest available year. The data refers to 2022 for Australia, Belgium, Colombia, Costa Rica, Czechia, Estonia, Germany, Japan, Poland, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. The sample is all TL-2 regions with available data.
Source: OECD calculations based on the OECD Regional databases.
Regions with a high degree of take-up in part-time work do not necessarily also have more workers employed in short-term contracts, pointing to substitutability between part-time and short-term work. Annex Figure 1.B.11 plots the regional share of workers in part-time work contracts versus the regional share of workers in temporary employment. The correlation of these two variables, accounting for country-specificities, reveals a lack of relationship between the two. Furthermore, there are clear clusters of regions in the same country, which indicates that structural national policies likely shape employment practices in the take-up of different types of non-traditional work.
Note: The figure shows the share of employment in fixed-term contracts on the x-axis and the share of employment in part-time work on the y-axis in the year 2023 or latest available year for OECD regions with available data. For part-time work, the data refers to 2022 for Finland and Iceland; and to 2021 for Romania. For the temporary work rate, the data refers to 2022 for Australia, Belgium, Colombia, Costa Rica, Czechia, Estonia, Germany, Japan, Poland, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom. The dotted line represents the correlation line, and the grey shaded area is 95% confidence intervals between the two measures. Each dot represents a region.
Source: OECD calculations based on the OECD Regional databases.
← 1. For more data on NEET rates, see Annex Figure 1.B.3 and Annex Figure 1.B.4.
← 2. This is despite the worries that the gendered impact of lockdowns, as described above, contributed to an initial worry of a “she-cession” especially as initial indicators saw a disproportionate drop in female labour market participation at the onset of the crisis (Landivar et al., 2020[87]).
← 3. Measuring skill mismatches poses several challenges as it requires quantifying both the skills of workers and the demands of their jobs, neither of which are observable. Thus, to quantify skill mismatch, this chapter proxies a workers’ skill level through their level of education, using the ISCED classification. The skill demands of the job are calculated as the most common skill level of workers in that occupation-country-year. Skill mismatch is thus present when the skill level of an individual worker does not match the skill level of their occupation. Over-(under-) skill mismatch is when the worker’s skill level is above (below) their occupation’s skill level. The advantage of this approach is that it only requires information on the educational level and occupation of workers, both of which are readily available in most labour force surveys. Furthermore, by defining the occupational skill level relative to the country and year, the approach takes into account cross-country differences and changing skill demands of an occupation.
← 4. Note that it is not possible to look at the demographics of self-employment take-up, i.e. the share of women, male, youth, elderly, immigrants, natives, etc. engaged in self-employment due to issues of small sample sizes in labour force surveys. The Chapter focuses only on overall rates of self-employment among the employed.
← 5. The information on mass layoffs comes from a database on large restructuring events reported in the principal national media and company websites, collected by Eurofound. It provides information on the instance of mass layoffs, defined as the announced destruction of at least 100 jobs or that affects at least 10% of the workforce at sites employing more than 250 people. Importantly, this database is not representative of mass layoff events as the size requirement leads to a bias towards medium and large firms, especially in the manufacturing sector. Nonetheless, it is a useful resource to get an overall idea of their instance across OECD regions.