Exploring labour-market vulnerability and resilience
Exploring labour-market vulnerability and resilience
Introduction
Although the Nordic labour market performs fairly well overall, there are substantial differences between municipalities and between regions across the Nordic countries. Chapter 5 illustrated this through developments in employment and unemployment, while Chapter 6 highlighted the strong role played by the public sector in the Nordic welfare model as well as the expansion of business services. These territorial differences provide an important backdrop for understanding how well different parts of the Nordic Region are positioned to manage the future impacts of economic change on the labour market.
This chapter examines local and regional labour-market vulnerability and resilience. Previous research shows that the Nordic labour markets usually span over several municipal borders (Borges, 2020), and shaped by regional history, culture, institutions, economic structure and the composition of labour. Efficient and flexible labour markets can strengthen regional competitiveness and support economic and social resilience (Borsekova & Korony, 2023). While 'vulnerability' and 'resilience' are complex concepts, utilised in a range of fields, in this context, vulnerability refers to a regional labour market’s robustness. In contrast, resilience describes its capacity to respond to changing circumstances, including external shocks and economic disturbances.
Borsekova and Korony (2023) identify three indicators as particularly important for labour-market vulnerability and resilience: high employment, a small gender gap and high productivity. While the first two indicators were examined in Chapter 6, this chapter analyses labour-market productivity, alongside two other lenses: occupational skills and sectoral employment concentration. These lenses reflect key dimensions of regional labour-market structure, which are particularly relevant for understanding vulnerability and resilience in the Nordic context. Together, they provide insight into the capacity of the Nordic labour markets to adapt to changing economic conditions and emerging challenges.
Labour market productivity
Labour productivity is an important indicator of economic growth and competitiveness. Figure 7.1 shows labour productivity in the Nordic Region and the EU between 2005 and 2025, measured as GDP per employed. There is a clear upward trend over the past two decades, with the exception of the years following the 2009 financial crisis and the COVID-19 pandemic. It is notable that, despite being more susceptible to fluctuations, the smaller economies perform relatively well.
Figure 7.1: Real labour productivity per person employed 2005-2025 in euros in constant (2020) prices. Source: The Annual Regional Database of the European Commission (ARDECO, 2025c, 2025a, 2025b), Statistics Faroe Islands (2025a, 2025b), Statistics Greenland (2025a, 2025b). |
Figure 7.2 shows the average annual change in real labour productivity at both the national and regional (NUTS 2) levels. The capital region of Denmark records the most notable increase, largely driven by strong growth in the pharmaceutical sector, followed by Mellersta Norrland, benefitting from large industrial investments and a relatively large defence-industry presence. Over the past five years, Finland has been the only Nordic country to experience a negative average annual productivity change (-0.8%). Although nearly all Norwegian regions experienced declines, Norway’s national labour productivity still increased due to offshore activities such as oil and gas extraction. Without these offshore industries, Norway’s average productivity change would have been -1.7%.
Figure 7.2 also shows that average productivity growth in the Nordic Region has lagged behind the European average. Weak productivity growth is a concern across Europe, where missed opportunities in digital innovation have widened the productivity gap with the United States. This has significant implications for European competitiveness (Draghi, 2025). Because productivity influences a region’s long-term capacity for adaptation, investment and innovation, differences in productivity growth also signal differences in labour-market vulnerability and resilience.
Figure 7.2: Average yearly change 2019-2024 in real labour productivity per person employed at the national level and NUTS2 level. Source: The Annual Regional Database of the European Commission (ARDECO, 2025c, 2025a, 2025b), Statistics Faroe Islands (2025a, 2025b), Statistics Greenland (2025a, 2025b). *Norway: Including extra-territorial activities |
Occupational skills
Ensuring a good match between supply and demand for skills is a key element in reducing regional vulnerability and increasing labour market resilience. Although the notion of supply and demand of skills has been criticised as an oversimplification (Buchanan et al., 2017; Lundgren & Meijer, 2025), it remains a useful starting point for understanding labour market performance.
In this section, the focus is on occupational skills. While the definition of skills depends on perspective and context (Bryson, 2017; Toner, 2011), it is common to distinguish between general (generic) skills, job-related skills, and company-related skills (Gambin et al., 2016). With a definition used by the International Labour Organisation (ILO), 'Occupational skills' (ISCO) refers to the nature and complexity of tasks and duties performed in an occupation, as well as the level of formal education, informal on-the-job training and/or previous work experience required (ILOSTAT, n.d.). In this chapter, occupational skills are grouped into three different categories: high-, medium- and low-skill occupations.
In the analysis, we use the Major Occupational Groups (ISCO 1-digit level), ranging from groups 1 to 9. Group 0 (Armed Forces Occupations) is excluded due to lack of data. High-skill occupations include managers, professionals, technicians and associate professors. Medium-skill occupations consist of clerical support workers; service and sales workers; skilled agricultural, forestry and fishery workers; craft and related trades workers; and plant and machine operators and assemblers. Finally, low-skill occupations correspond to elementary occupations (ILOSTAT, n.d.).
Figure 7.3: Shares of high-, medium- and low-skill occupations, 2014-2023. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs) and Nordic Statistics database. Data for Denmark, Finland, Iceland, Norway, Sweden and Åland: 2014 and 2023; Norway: 2015 and 2024, and the percentage changes between these years. No data is available for the Faroe Islands or Greenland. Data for Sweden 2014 has been adjusted to account for the transition in labour market statistics from RAMS to BAS. 'Nordic Region' is calculated based on data for Denmark, Finland, Iceland, Norway, Sweden and Åland. |
Across all of the Nordic countries, the composition of occupational skills has shifted notably over the past decade. The share of employment in high-skill occupations has increased, while the share of medium-skill occupations has declined. In 2014, the majority of the workforce in Denmark, Iceland and Norway were already in high-skill occupations (46.5%, 48.5% and 51.1% respectively), whereas the largest shares in Finland, Sweden and Åland were in the medium-skill category (49.8%, 49.3% and 55.9% respectively). This trend is consistent across the Nordic countries, although the magnitude varies. The most pronounced increases in high-skill shares are seen in Iceland and Åland (+6.2% and +6.1%, respectively), both of which also show the steepest declines in medium-skill shares (both at -4.8). The share of low-skill occupations has remained relatively stable across all of the countries, which may indicate that these jobs are less susceptible to automation (Borges, 2020).
These trends suggest two mutually reinforcing processes. First, demand for high-skilled labour is increasing, particularly in business services and in professional, financial and technical occupations. Second, a larger share of the population now has a tertiary education, which both enables and accelerates this shift towards high-skill employment. As global competition intensifies and the economy becomes more knowledge-intensive, this development suggests that the Nordic labour markets overall are reducing their vulnerability and strengthening their robustness and resilience to economic change.
Map 7.1: Change in share of population (20-64) in high skill occupations, 2014-2023. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs) and Eurostat. pp=percentage points High-skilled defined as ISCO 1-3; i.e. managers, professional, or technicians and associate professionals. No data for Faroe Islands and Greenland. National level data for Iceland. Data for Sweden 2014 has been adjusted to account for the transition in labour market statistics from RAMS to BAS. Nordic average: +3.2 |
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Patterns at the municipal and regional levels mirror national developments. Most municipalities have seen increases in the share of high-skill occupations, while very few record declines. At the regional level, the largest increases were in Pirkanmaa, Central Finland, South Karelia, Åland and Iceland.
Map 7.2: Change in share of population (20-64) in medium skill occupations, 2014-2023. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs) and Eurostat. Medium-skilled is defined as ISCO 4-8; i.e. cleric support workers, service and sales workers, skilled agricultural, forestry and fishery workers, craft and related trades workers, or plant and machine operators, and assemblers. No data for the Faroe Islands and Greenland. National level data for Iceland. Data for Sweden 2014 has been adjusted to account for the transition in labour market statistics from RAMS to BAS. Nordic average: -3.0 |
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Conversely, there has been an overall decline in medium-skill occupations across the Nordic Region. The vast majority have experienced decreases, with just a few, very small municipalities recording increases above 5 percentage points (Geta, in Åland, Modalen and Utsira in Norway). At the regional scale, all Nordic regions show shrinking medium-skill shares, although in some areas the declines are somewhat weaker.
Map 7.3: Change in share of population (20-64) in low skill occupations change, 2014-2023. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs) and Eurostat. Low-skilled is defined as ISCO 9; i.e. elementary occupations. No data for the Faroe Islands and Greenland. National level data for Iceland. Data for Sweden 2014 has been adjusted to account for the transition in labour market statistics from RAMS to BAS. Nordic average: -0.2 |
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The changes in low-skill occupations are less pronounced, partly because this is a small category in ISCO terms. Nevertheless, clear spatial differences exist. Notable increases (over 5 percentage points) are observed in Tydal (Norway) and the Blekinge region (Sweden), where all municipalities except Olofström increased their shares of low-skill occupations. The declines in low-skilled occupations are concentrated in northern Finland, northern Sweden and Åland, often in areas that simultaneously display strong growth in high-skill occupations, which is indicative of ongoing structural shifts toward more knowledge-intensive labour markets. However, despite technological change and increased demand for higher skills, not all jobs require high or medium skills, which may help explain the relative stability of low-skill occupations in the Nordic Region. As discussed by Borsekova and Korony (2023), as well as in Chapter 6, gender segregation constitutes an important source of vulnerability in the Nordic labour markets. Research shows that gender segregation results in distinct occupational outcomes for men and women: women are increasingly moving into higher-skilled roles, especially in the public sector, and expanding their presence in the top wage quintile; while men are facing losses in mid-tier jobs, alongside growth in the lower quintile (Berglund et al., 2025; Ulfsdotter Eriksson et al., 2022). These patterns underline the need to integrate gender perspectives when assessing and analysing vulnerability and resilience in Nordic labour markets.
Sectoral employment concentration
A third lens for analysing labour-market vulnerability and resilience is the degree to which employment is concentrated in a small number of industry sectors. Research shows that industrial specialisation can contribute to productivity and economic growth. However, when employment is concentrated in a small number of sectors, it may also increase vulnerability to economic disruption. A sectoral employment concentration index has therefore been developed to evaluate this metric in Nordic municipalities.
Municipalities with high shares of total employment concentrated in a small number of industry sectors score high on the index (shown in dark purple on Map 7.4), while municipalities with more evenly distributed employment across several sectors score low (light purple on Map 7.4). Since the index is based on the residential (night-time) population, it also reflects the functionality of wider labour-market regions, as many individuals commute across municipal and regional borders.
Municipalities with high shares of total employment concentrated in a small number of industry sectors score high on the index (shown in dark purple on Map 7.4), while municipalities with more evenly distributed employment across several sectors score low (light purple on Map 7.4). Since the index is based on the residential (night-time) population, it also reflects the functionality of wider labour-market regions, as many individuals commute across municipal and regional borders.
Box 7.1: Sectoral employment concentration index
The sectoral concentration index is applied to the Nordic municipalities where sufficiently detailed data has been available (i.e., Denmark, Finland, Norway, Sweden). The map and the box plot graphs present this data in visual form.
The concentration index is calculated as the standard deviation of the percentage distribution of employment across industry sectors on NACE 3-digit level, where each sector has a share of total employment. Standard deviation, which is the average deviation from the variable's mean, is a commonly used measure of statistical dispersion.
Other measurements of concentration and diversity – such as the Herfindahl Hirschman Index, GINI coefficient and number of industry sectors – are strongly correlated to our use of standard deviation in the Nordic municipalities. Due to the National Statistical Institutes’ confidentiality policies, industry sectors that employ three or fewer people have been excluded from the calculations.
Map 7.4: Sectoral employment concentration, 2023. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs) and Eurostat. No data for the Faroe Islands, Greenland, Iceland and Åland. Nordic average: 1.0 |
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Sparsely populated areas characterised by long distances tend to have higher levels of employment concentration than larger urban regions. This is illustrated in Map 7.4, where municipalities in predominantly rural areas with low accessibility show less diversified employment structures. Conversely, metropolitan and more densely populated areas, such as southwestern Finland and most of Denmark, are more diversified.
However, Map 7.4 also indicates that even within regions characterised by relatively high sectoral diversity, individual municipalities may exhibit strong concentration in a few sectors, which can increase vulnerability unless they are integrated into larger labour-market regions.
Figure 7.4: Box plot showing the sectoral employment concentration by country. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs). |
A comparison between the Nordic countries
shows that municipalities in Denmark have the lowest level of sectoral employment concentration (i.e., more diversified structures) and relatively small differences between municipalities. Sweden displays a slightly broader and higher range than Denmark, while Finland and Norway show wider spreads of values, indicating higher concentration and greater variation in specialisation. Norway has both the broadest range and the largest number of outliers, which reflects the substantial differences between municipalities – some of which have diversified labour markets, while others are highly specialised, with only a few industry sectors.
The box plot illustrates industrial employment concentration in Denmark, Finland, Norway and Sweden. A box plot displays the minimum and maximum non-outlier values (the “whiskers”), the median (the line inside the box), the lower and upper quartiles (the edges of the box), and any outliers (points beyond the whiskers). In this graph, “X” also marks the mean value.
A closer examination shows that the outlier municipalities differ markedly in character. Examples include rural municipalities such as Vaerøy and Røyrvik (Norway) and Lestijärvi and Luhanka (Finland), as well as Kalundborg (Denmark), which hosts the large Novo Nordisk facility, and Strömstad (Sweden), where employment is strongly tied to cross-border trade.
Figure 7.5: Box plot showing the sectoral employment concentration by Nordic urban-rural typology. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs). |
As shown both in Map 7.4 and Figure 7.5, which classifies municipalities by Nordic urban-rural typology (Stjernberg et al., 2024), sectoral employment concentration is highest in rural municipalities and lowest in urban ones. This indicates a clear relationship between diversity of industrial employment and population density.
Urban areas display the greatest sectoral diversity, with a narrow statistical range and 8 outliers, compared to 9 and 12 in intermediate and rural areas respectively. This reflects the fact that larger, more accessible and interconnected labour-market regions are less dependent on individual industry sectors. They also benefit from a wider range of economic functions and institutions, as well as greater accessibility, which further increases labour mobility and can help mitigate the effects of concentration and enhance regional resilience (Onsager & Tønnessen 2012; Koster et al. 2020).
At the other end of the spectrum, rural municipalities show the highest sectoral concentration, expressed in both the highest median and mean values and the widest overall spread. These labour markets are smaller, and often shaped by primary industries or geographically specific activities. Demographic challenges, such as population decline and ageing, further restrict labour availability and reduce opportunities for diversification.
Intermediate municipalities show a broader range than urban areas, and include several outliers. However, their median and mean concentration values are close to those of urban municipalities, which indicates that they are comparatively diversified. Many intermediate municipalities are geographically located near urban centres and, as such, are integrated into the same local labour markets.
Overall, the index shows that the concentration of employment in a few industry sectors is more common in rural areas than in urban areas. In many cases, this reflects the area’s industrial history and access to natural resources, as well as the economic and institutional capacity to build on these assets. While industrial specialisation can support productivity and economic growth, it may also increase vulnerability to economic disturbances. As Map 7.4 shows, municipalities with greater access to larger labour-market regions can compensate for high concentration levels, and may thereby reduce their vulnerability.
Conclusions
In their analysis of labour-market vulnerability and resilience, Borsekova and Korony (2023) highlight three indicators: employment, gender balance and productivity. As discussed in Chapter 5, while employment levels in the Nordic Region are generally high, gender segregation remains a persistent feature of the Nordic labour markets. This chapter analysed productivity alongside two additional lenses: occupational skills and sectoral employment concentration.
The productivity analysis shows that, in terms of productivity, the Nordic countries and regions performed well compared to the EU during 2005–2024. However, when it comes to productivity growth during the same period, some countries outperformed the EU while others showed weaker growth. The pattern is similarly varied in the most recent five-year period (2019–2024), with considerable disparities between regions. However, according to classical economic theory, the increase in high-skill occupations observed across the Nordic Region, as well as the growth of knowledge-intensive industries and business services (as described in Chapter 6), could be expected to have a positive impact on productivity. There are several potential explanations for why that is not the case, including the strong dominance of the public sector, intensified global competition and rapid technological change, all of which have significant implications for the labour market. The findings therefore underline the importance of monitoring productivity and its role in shaping the robustness of the Nordic municipalities and regions.
The two other lenses, occupational skills and sectoral employment concentration, reveal spatial patterns with implications for local and regional vulnerability and resilience. First, the increase in high-skill occupations during 2014–2023 occurs largely at the expense of medium-skill occupations, and this pattern is not confined to urban or university regions. This indicates a broader shift toward more knowledge-intensive labour markets and rising education levels across the Nordic Region. Second, the sectoral employment concentration index shows that urban municipalities typically offer employment opportunities across a wider range of industry sectors, whereas in rural municipalities, employment tends to be concentrated in fewer sectors. While specialisation can be a source of competitiveness, it may also increase vulnerability to economic disruptions, particularly in rural areas or municipalities that are not integrated into larger labour-market regions.
Taken together, these findings illustrate how different dimensions of labour-market structure intersect to shape vulnerability and resilience. They highlight the value of using complementary indicators, tracking developments over time and examining patterns across multiple spatial scales in order to understand local and regional labour-market dynamics better.
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