Maps

This map shows the result of Nordregio’s Regional Potential Index in 2024 (data from 2022). Nordregio’s Regional Potential Index (RPI) enables cross-regional comparison of development potential and illustrates the regional balance between the Nordic countries and has been part of the State of the Nordic Region report since 2018. The purpose of this multidimensional index is to summarise the current and past performance of the Nordic regions across major policy domains. The index helps to identify regions that have high potential and those in need of further support to boost their potential and meet existing challenges. It provides policy-makers with a comparative learning tool that informs the design of effective regional development strategies at Nordic level. Nordregio’s RPI is a multi-item measurement scale that incorporates information about the demographics, labour market and economic output of the Nordic countries’ 66 administrative regions. It consists of eight indicators classified into four main groups and eight subgroups. These components and indicators were originally selected on the basis of their relevance for regional development. The 2024 RPI is based on a new refined method that maintains a similar set of indicators but applies a more robust statistical process to the construction of the RPI. In brief, the new methodology consists of a pre-processing stage, in which the input data is prepared for analysis, and a processing stage, in which the indicators are weighted and aggregated. More information about the method can be found in the State of the Nordic Region 2024 report. The RPI was calculated retroactively for the 2015–2023 period. However, the focus in this section is on 2022 – the most recent year in our time series with full data coverage. The map shows the redesigned RPI for that period. In line with the principles of accumulation and agglomeration that drive the…

This map shows the percentage change in household disposable income between 2018 and 2022 in Nordic municipalities (big map) and regions (small map). Household disposable income per capita is a common indicator of the affluence of households and, therefore, of the material quality of life. It reflects the income generated by production, measured as GDP that remains in the regions and is financially available to households, excluding those parts of GDP retained by corporations and government. In sum, household disposable income is what households have available for spending and saving after taxes and transfers. It is ‘equivalised’ – adjusted for household size and composition – to enable comparison across all households. Purchasing Power Standards (PPS) is used to compare the countries’ economies and the cost of living for households. As shown in the map, between 2018 and 2022, household disposable income increased for all Danish, Icelandic, and Norwegian municipalities and decreased for Finnish and Swedish municipalities. On average, the city municipalities have higher incomes and increased most in Finland and Sweden in 2018–2022. In Sweden, a tendency towards larger falls in income was observed in several southern municipalities. In summary, absolute household income increased in all Nordic countries but not when measured in purchasing power. Based on this metric, on average, Norwegian households are the most well-off and Iceland the worst off, while Danish households benefited from a stronger currency in 2022. Single-parent households have had lower increases in household income than other families in Norway and parts of Sweden. Municipalities show a similar trend in Norway and Denmark, although Norwegian coastal municipalities fared slightly better in 2022. Disposable income is falling in all Swedish and Finnish municipalities.

This map shows the median household disposiable income measured in purchasing power standard (PPS) in Nordic municipalities (big map) and regions (small map) in 2022. Household disposable income per capita is a common indicator of the affluence of households and, therefore, of the material quality of life. It reflects the income generated by production, measured as GDP that remains in the regions and is financially available to households, excluding those parts of GDP retained by corporations and government. In sum, household disposable income is what households have available for spending and saving after taxes and transfers. It is ‘equivalised’ – adjusted for household size and composition – to enable comparison across all households. Purchasing Power Standards (PPS) is used to compare the countries’ economies and the cost of living for households. The map shows the intra-municipal differences in household disposable income in PPS, which reveals the different patterns in the Nordic countries. Norwegian coastal municipalities have slightly higher household disposable income than inland municipalities, with some exceptions. Finnish, Icelandic, and Swedish municipalities generally have much lower household disposable income compared to Norwegian and Danish municipalities, except for the larger urban areas. In Denmark, most municipalities are at a similarly high level, except for remote islands in the south. The differences between the Icelandic municipalities are rather small, at a medium to lower level.

This map depicts the change in total number of bankruptcies in the Nordic regions between 2020 and 2022. The red shades indicates an increase in numbers of bunkruptcies and blue shades a decrease. The big map shows the regional level and the small map the national level. The rate of business bankruptcies is a core indicator of the robustness of the economy from the business perspective. Nordic and international businesses have been impacted by both the COVID-19 pandemic and rising inflation in recent years. In terms of the level of bankruptcies, data from Eurostat (2024) shows that the Nordic countries fared relatively well compared to other high-income countries between 2020 – 2022. In the years during and after the COVID-19 pandemic, the most densely populated regions saw the highest levels of bankruptcies. This finding is partly to be expected, as these regions also tend to be those with the highest number of companies. However, some variation can be seen across the countries. Overall, Iceland and Finland experienced the lowest rate of bankruptcies in 2020 and 2022. Denmark had the highest level of bankruptcies during COVID-19. Potential explanations for the national variations may include the countries’ varying strategic approaches to the pandemic. Denmark enforced more restrictive lockdowns compared to, for example, Sweden, where the less restrictive approach has been linked to the more limited impact on business bankruptcies in the early part of the pandemic. Furthermore, there is a large consensus that the many jobretention schemes across the Nordic Region also served to limit the number of bankruptcies. However, new data from early 2024 shows that after the job-retention schemes ended, and while high inflation and interest rates were increasing the pressure on Nordic companies, the level of bankruptcies increased. In 2023, 8,868 companies went bankrupt in Sweden the highest number…

This map shows the percentage change in the Gini coefficient between 2018 and 2022. The big map shows the change on municipal level and the big map at regional level. Blue shades indicate a decrease in income inequality, while red areas indicate an increase in income inequality The Gini coefficient index is one of the most widely used inequality measures. The index ranges from 0–1, where 0 indicates a society where everyone receives the same income, and 1 is the highest level of inequality, where one individual or group possesses all the resources in the society, and the rest of the population has nothing. The map illustrates significant variations in the change in income inequality across Nordic municipalities and regions. Between 2018 and 2022, income inequality increased in predominantly rural municipalities, notably in Jämtland, Gävleborg, Dalarna and Västerbotten in Sweden, as well as Telemark in Norway. For Denmark, the rise in inequality is mainly for the municipalities in Western Jutland. At the same time, approximately one third of municipalities in the Nordic Region experienced a decrease in income inequality during the same period, primarily in Finland and Åland. For example, in Finland, the distribution of inequality was more varied. This trend aligns with the ongoing narrowing of the household income gap observed in many Finnish municipalities since 2011, which is mainly attributed to the economic downturn of the early 2010s, as well as demographic shifts such as outmigration and ageing.

This map shows the Gini coefficient in Nordic municipalities (big map) and regions (small map) in 2022 (no data was available for Iceland). Blue shades indicate a Gini coefficient below the Nordic average. Red areas indicate a Gini coefficient above the Nordic average (0.27, excluding Greenland, as a statistical outlier). The data for the Faroe Islands is for 2021. The Gini coefficient index is one of the most widely used inequality measures. The index ranges from 0–1, where 0 indicates a society where everyone receives the same income, and 1 is the highest level of inequality, where one individual or group possesses all the resources in the society, and the rest of the population has nothing. In 2022, the highest municipality income disparities were observed in the capital city regions of Denmark, Finland and Sweden, each of which had Gini coefficients around 0.6. Danderyd (0.64), Lidingö (0.52), and Gentofte (0.51) had the highest Gini coefficients. These municipalities also have some of the highest incomes in their respective countries.

This map shows the tonnes of greenhouse gas emissions per person employed in Nordic municipalities (big map) and regions (small map) in 2022. The data for Iceland is presented at the national level, while no data was available for the Faroe Islands and Greenland. The map is based on data on emissions per sector and country from Eurostat and detailed employment by sector data from the Nordic statistical offices. By calculating the average emissions per person employed and per sector we could use municipal employment by sector data to assess the average emissions per person employed in each municipality. The results are an estimation based on the assumption that all jobs in the same sector have the same GHG emissions. In 2022, greenhouse gas (GHG) emissions per person employed in the Nordic Region were 15.7 tonnes. This is higher than the EU average of 13.5 tonnes. There are also fairly big differences between the Nordic countries, with higher emissions per person employed in Iceland (28.6), Denmark (23.1) and Norway (20.5) and lower emissions in Finland (15.7) and Sweden (8). On the other hand, the emissions per person employed have decreased faster in the Nordic Region than for the EU as a whole. In the last decade, emissions per person employed fell by 24% in the Nordic Region compared to the EU average of 22%. The biggest decrease (32%) was in Finland. The sectors with the highest emissions per worker vary slightly between the countries. In Sweden and Norway, the sector with by far the highest emissions per worker was the manufacture of petroleum coke and refined petroleum products. However, it should be noted that the number of workers in this sector is small. In Denmark, the highest emissions by person employed could be found in water transport; in Finland, in…

This map shows the share of people aged 25-64 years with a tertiary education in Nordic municipalities (big map) and regions (small map) in 2022. All of the Nordic countries have a higher share of people who completed tertiary education than the EU average. The highest share is in Sweden (48.6%), followed by Norway (47.8%), Iceland (42.9%), Finland (42.7%) and Denmark (42.1%). There are, however, big differences within the countries with a considerable urban-rural divide in terms of tertiary education. The capital regions of Oslo (66%), Stockholm (58%), Copenhagen (Hovestaden) (51%), Reykjavik (51%) and Helsinki (Uusimaa) (49%) stand out with particular high share of tertiary educated population. Conversely, rural regions such as Iceland outside of Reykjavik (30%), Kymenlaakso (34%), Kainuu (34%), Etelä-Savo (35%), Satakunta (35%) in Finland and Syddanmark (35%), Midtjylland (36%) and Nordjylland (36%) in Denmark are among the regions with the lowest share of tertiary educated population. The successful implementation of the Bologna Process and the derived increase in educational levels of young people in the Nordic Region coincides with large numbers of the ‘baby boomer’ generation leaving the labour market. This generation has a significantly lower level of education than the current 24–35 age group – i.e. those who are now entering the labour market. This trend can be seen across the Nordic Region.

This map displays the share of high-skilled workers as a share of the total number of workers in Nordic municipalities (big map) and regions (small map). “High-skilled workers” is here defined as group 1-3 (Managers, Professionals and Techinicians/associated professionals) of the International Standard Classification of Occupations (ISCO). For Iceland national data is used. The EU average of high-skilled workers is 43%, and the Nordic countries are at the top of the rankings – 49.5% in Finland, 51.1% in Denmark, 54.2% in Norway, 54.5% in Iceland and 58.9% in Sweden. On a regional level, the highest share is in the capitals and bigger cities, such as Stockholm (72%), Oslo (71%), Hovedstaden (Copenhagen) (60%), Uppsala (60%) and Uusima (Helsinki) (59%). The lowest shares are in the Finnish regions of EteläPohjanmaa, Keski-Pohjanmaa, Satakunta and Etelä-Savo (less than 40%). However, this does not necessarily mean that employers will have a greater chance of successfully recruiting high-skilled workers in the future, partly because those in this group already have jobs and partly due to generally lower investments in education.

This map shows the employment rate for the age group 55-74 years in the Nordic municipalities (big map) and regions (small map) in November 2022. Employment rate refers to the share of population in a particular age group that is in employment (LFS definition), in this map it is measured for the age group 55-74 years. Older adults (55+) were less affected by the pandemic. In general, this age group has a stronger attachment to the labour market, and a higher proportion work in occupations where remote work was possible. For quite some time, the Nordic Region has boasted a higher employment rate among older adult workers compared to the rest of Europe, although this gap has narrowed somewhat in recent years. In 2022, 75.5% of the Nordic population aged 55–64 were employed, whereas the EU average stood at 62.3%, representing a difference of 13 percentage points. In 2012, however, this difference was 21 percentage points, and in 2002, it was 29. The map shows variation between the Nordic countries, with Finland exhibiting a lower employment rate for this age group. The highest rates were observed in the Faroe Islands (62%), Greenland (61%), Åland (56%), and the Swedish regions of Jämtland-Härjedalen (55%) and Jönköping (54%). The Labour Force Survey (LFS) is the official source for labour market statistics and the only source that is comparable across countries. The data comes from a standardised survey that is conducted in the same way in all EU countries, as well as in many non-EU countries, including in Norway, Iceland and the Faroe Islands. The LFS is the main source for the map, however since it is not possible to break down at a municipal level, register data has been made to create estimates at a municipal level.

These maps shows the employment rate in 2022 for those born in a EU country (top left) and those born outside of the EU (bottom left), as well as the change in employment rate between 2020 and 2022 for those born in the EU (upper right) and outside the EU (lower right). The data is displayed at NUTS 2 level and comes from the labour force survey (LFS). The category ‘foreign-born’ is quite heterogeneous and consists of everything from labour migrants to refugees – two groups who face quite different conditions and have different connections to the labour market. The employment rate for people born in another EU country – a group that includes a large proportion of labour migrants – has been on par with the employment rate for native-born people for a long time. As can be seen in the top-left figure in the map, in 2022 all NUTS2 regions except Southern Denmark had an employment rate of 75% or more for this group. The highest employment rate was observed in the Swedish NUTS2 regions of Middle Norrland, Stockholm and Western Sweden, followed by Oslo in Norway and Iceland. The employment rate for people born outside of the EU (a group that largely consists of refugees) has been lower for a long time than that of native-born people and those born in the EU. While the employment rate for people born in non-EU countries is still lower than for natives (a 15 percentage point difference (pp) in Sweden, 11 pp in Norway, 7 pp in Denmark and Finland, and 2 pp in Iceland), this gap has been closing in the last couple of years since the pandemic. Between 2020 and 2022, the employment rate for those born outside of the EU rose almost eight percentage points in Denmark…

This map shows the employment rate for Nordic municipalities (big map) and regions (small map) in 2022 (measured in November). Employment rate refers to the share of population in a particular age group that is in employment (LFS definition), in this map it is measured for the age group 20-64 years. At a national level, all of the Nordic countries have employment rates higher than the EU average (74.6%). The highest rates were in the Faroe Islands (93%), Åland (86%) and Iceland (83%), followed by Sweden (82.2%), Denmark (80.1%), Norway (80.9%), Finland (78.4%) and Greenland (75%). Of the EU countries, only the Netherlands (82.9%) had a higher employment rate than Sweden in 2022. As the map shows, most of the regions and municipalities had employment rates above 75% (the green areas on the map) in 2022, meaning that three-quarters of people aged 20–64 were working. In addition to the Faroe Islands, Iceland and Åland, the Swedish regions of Jämtland-Härjedalen, Halland and Jönköping also had employment rates of more than 85%. The only regions with employment rates under 75% were the Finnish regions of North and Southern Karelia and KeskiSuomi. On a municipal level, the highest employment rates were found in rural municipalities, mainly in Iceland and the Faroe Islands, as well as some municipalities in Österbotten in Finland (such as Pedersöe and Korsholm). Of the intermediate municipalities (between urban and rural), the highest employment rates were in the Swedish municipalities Habo (Jönköping), Hammarö (Värmland), Nykvarn (Stockholm), Gällivare (Norrbotten), Kungsbacka (Halland), Svedala (Skåne) and Mosfellsbær (Höfuðborgarsvæðið) in Iceland. Of the urban municipalities, the employment rate was highest in Garðabær and Kópavogur in the Capital Region of Iceland, Tyresö and Täby in the Stockholm region, Partille in Västra Götaland in Sweden, and Bærum and Rælingen in Viken in Norway. The lowest employment…

A map portrays tertiary education attainment level around the Nordics, based on a grid level data.

A map portrays tertiary education attainment level around the Nordics.

A map portrays a new urban-rural typology based on the grid-level data. New Nordic urban-rural typology is a grid-based classification of areas developed by the Nordic Thematic groups 2021-2024 to enable more accurate cross-Nordic statistical comparisons. The seven classes are defined based on population density, proximity measures and land cover parameters. Read more about the typology here . Inner urban area is the most densely populated part of the urban core. Urban cores are clustered cells summing up to at least 15 000 inhabitants, and these are divided into Inner and Outer urban areas based on density criterion (population density and building floor space). Outer urban area is the least densely populated part of the urban core. Urban core areas are clustered cells with at least 15 000 inhabitants, and these are divided into Inner and Outer urban areas based on density criterions (population density and building floorspace). Peri-urban area is the intermediate zone between urban core and the rural. It is based on generalized travel-time estimates from the edges of outer urban areas (6 min travel-time zones) and smaller urban settlement (4,5 min travel-time zones). Local centers in rural areas are population centers located outside urban areas, small towns and large parish villages where population is between 5000-14999 inhabitants. Rural areas close to urban areas have a rural character that are functionally connected and close to urban areas. In average this means 20-30 of minutes’ drive time from the edge of outer urban area. This class overwrites the area classes ‘Rural heartland’ and ‘Sparsely populated rural areas’.  Rural heartland. Rural areas with intensive land use, with a relatively dense population and a diverse economic structure at the local level. Most of the agricultural land is in this class. Sparsely populated rural areas. Sparsely populated areas with dispersed small settlements that are located at a distance from each other.…

The map portrays the urban-rural typology based on the municipal level data.

This map shows the internal net migration rate in 2018 (left map) and net migration by age group (right map) in local labour market areas of the Northern sparsely populated areas in 2018 The map on left shows the internal net migration rate in the NSPAs in 2018. Although many regions experienced negative net migration, this was not the case for all segments of the population. The map on the right of shows the age groups in which more people moved in than moved out, despite these regions having negative net migration overall. The red colours on the map on the left indicates a net out-migration, while the blue colours indicate a positive net migration. In the map on the right, the red colours indicate net out-migration for all age groups and the grey positive net migration overall. The other colours indicate age groups with a positive net migration (while having negative net migration overall). Inward and outward migration are slightly different in the local labour market areas (LLMAs) compared to the independent labour markets. Among the LLMAs, Rovaniemi is the only one that has positive net migration in all age groups. This positive development may be due to the number of governmental offices, the strength of the tourism industry and the presence of two universities. On the contrary, 18 local labour market areas– four in Sweden, nine in Norway and five in Finland – have negative net migration in all age groups. Out-migration of young adults is seen in most of LLMAs, the exceptions being Umeå, Målsev, Joensuu and Oulu. The inflows of young adults in these regions reflect the presence of the universities in Umeå, Joensuu and Oulu and the army bases in Målselv. Despite this positive inflow, most of these LLMAs are unable to retain adults in…

Number of cruise ship passengers and cruise ship calls

Persons with at most lower secondary education as share of total population aged 18-24 The map indicates variations within the Nordic countries with the lowest rates of early school leaving found in Swedish regions. In six of the eight Swedish NUTS 2 regions (riksområden) early school leavers make up less than seven percent of the population aged 18-24 years. Hovedstaden in Denmark is the only other Nordic region with a similar rate. In Finland (suuralue/storområde) and Norway (landsdel), variations between the NUTS 2 regions are bigger than in Sweden and Denmark. In the northernmost Finnish region, Pohjois- ja Itä-Suomi (North & East Finland), early school leavers make up a small portion of the population aged 18-24 years, below both the EU average (11.1%) and the two southernmost Finnish NUTS 2 regions. In the Faroe Islands, Iceland, Northern Norway, and, in particular, Greenland, the percentage of the population aged 18-24 years with lower-secondary school as their highest level of education is well above the EU average. With respect to gender, the balance is fairly even in northern Sweden and northern Norway. All other countries and regions included in the map show patterns consistent with the EU average, with males making up a larger portion of the early school leaving population.