Nordic Population Diversity by Country of Birth
Nordic Population Diversity by Country of Birth
Introduction
This chapter examines the evolving landscape of population diversity in the Nordic countries through the lens of place of birth. Drawing on data from National Statistical Institutes (NSIs) spanning three decades (1990–2024), it investigates how diversity varies across countries, within national territories, and over time. The chapter provides an empirical account of these patterns and how they have changed. The central question is how these patterns differ across regions and between the Nordic countries.
Historically, the Nordic countries were among the more ethnically homogeneous societies in Europe (de Haas, Castles & Miller, 2020). However, from the 1980s onward, the region has experienced significant shifts in migration, with substantial variation in both scale and type (Heleniak, 2018; Ho & Shirono, 2015). Increased labour migration, humanitarian resettlement, EU mobility and global displacement have contributed to a growing foreign-born population. Despite shared characteristics, the Nordic countries display distinct trajectories. Norway, Sweden, Finland and Denmark have all experienced rising immigration, albeit shaped by different migration policies and social contexts, while Greenland had a relatively stable migration pattern. Sweden emerged as an early destination for refugee resettlement and labour migration, while other Nordic countries followed similar patterns more gradually.
This chapter focuses on national migration trends from 1990 to 2024 and provides an overview of diversity by country of birth at the municipal level. The data presented refer to migrant stock at different time points – the number of migrants residing in a country, measured by place of birth. By contrast, flows refer to movement into or out of a country during a specific period. While flows capture migration dynamics, stock data give a longer-term perspective on the composition of resident migrant populations (United Nations, 2012).
Migration to the Nordic Region over the last 30 years
Figure 4.1 presents Sankey diagrams showing the distribution of migrants in the Nordic countries by broad regions of birth in 1994
, 2004, 2014 and 2024. The width of each segment reflects the size of each origin group in the total foreign-born population. This allows readers to trace both the growth of migrant stocks and the changing relative importance of different regions of origin. The diagrams rely on stock data, which provide consistent, long-term measures of the composition of migrant populations across countries and years. Although stock data do not directly measure flows, changes in the diagrams across the benchmark years implicitly reflect the growing inflow of specific migrant groups into the Nordic countries.
For Iceland, Sweden and Åland, data for 1994 were not available; therefore, we used the closest available year.
Box 4.1: Methodology – Classifying Foreign-Born Status and Origin Groups
In this chapter, 'foreign-born' refers to individuals born outside of the country under analysis, irrespective of parental origin or citizenship. This clarification ensures comparability across the Nordic countries, although the statistical offices employ slightly different categorisations. For instance, Statistics Norway’s classification also incorporates generational birthplace (including parents and grandparents).
Country of birth information is grouped into 13 mutually exclusive origins, based on the United Nations (2020) region classification. The groups are the following: Native Population, Nordic Countries (Sweden, Norway, Denmark, Finland, Iceland, Greenland, Åland, and Faroe Islands), EU27 (excluding Nordic countries), Rest of Europe, Northern Africa and Western Asia, Sub-Saharan Africa, Eastern and Southern Asia, Central and Southern Asia, Northern America, Latin America and Caribbean, Oceania, Stateless/Unknown, and Other Countries (see also Table 4.1). The classification is based on the final year of observation (2024), such that countries are assigned to consistent categories across all time points. For example, Bulgaria, Romania, and Croatia were classified as EU27 members in 1994, despite not having acceded to the European Union at that time. There are minor differences across the countries due to variations in how NSIs report low-count populations or classify cases of “unknown” birthplace, which mainly affects municipal-level results. In Iceland, citizenship data were used at the municipal level for reasons of data availability.
Greenland and Åland use broader birthplace categories, which means that adapted classifications are required for these territories. As a result, their diversity values should not be directly compared with countries with detailed place-of-birth data. See Appendix 4.1 for the full list of countries included in each origin group.
Region Name | Acronym |
Native population | NAT |
Nordic countries | NORD |
EU27 (excluding Nordic countries) | EU27 |
Rest of Europe (excluding EU27 and Nordic countries) | EUR |
Northern Africa and Western Asia | NAWA |
Sub-Saharan Africa | SSA |
Eastern and South-Eastern Asia | ESEA |
Central and Southern Asia | CSA |
Northern America | NA |
Latin America and the Caribbean | LAC |
Oceania | OCE |
Stateless/Unknown | UNK |
Other countries | OTH |
Table 4.1. Country group classification. |
Shared trends in Nordic migration patterns
Figure 4.1 reveals both shared trajectories and important country-specific variations. Four salient trends characterise the Nordic Region’s migration stocks as a whole between 1994 and 2024. First, the number and share of foreign-born residents have increased across all Nordic countries since 1990.
In the early 1990s, the foreign-born share ranged from around 2.0% in Finland to 9.3% in Sweden. By 2024, these proportions had risen to 9.5% in Denmark, 10.4% in Finland, and 18.2% in Norway, and markedly higher in Sweden. Iceland also experienced rapid growth during this period, with the number of foreign-born residents rising from around 12,500, representing 4.6% of Iceland’s population to nearly 81,800 (21.3%). These developments reflect the broader shift toward migration-driven population growth in the Nordic Region (Heleniak, 2018).
It should be noted that the proportions presented here may not correspond precisely to the official migrant shares reported by the National Statistical Institutes (NSIs) of the Nordic countries. In several Nordic contexts, the term “foreign-born” encompasses multiple generations and is frequently cross-validated with citizenship status, resulting in figures that diverge from those presented in this analysis. Due to data availability constraints, the initial year of observation varies: Sweden (1990), other Nordic countries (1994), and Iceland (1998).
Second, the relative significance of intra-Nordic migration has declined markedly across all five Nordic countries. In the early 1990s, migrants from other Nordic countries constituted one of the largest migrant groups in every country except Denmark, reflecting long-standing traditions of labour mobility, bilateral agreements and cultural proximity (Lundgren et al., 2024). By 2024, the proportion of intra-Nordic migrants had diminished substantially, and they now account for less than 10% of the foreign-born population in all Nordic countries except the self-governing territories. This does not indicate a decline in absolute numbers. On the contrary, the absolute numbers have increased in all Nordic countries except Sweden. However, their relative share compared to other origin groups has declined. In the Faroe Islands, Greenland, and Åland, intra-Nordic migrants remain the predominant migrant group, illustrating how historical ties and regional mobility patterns continue to shape population composition.
Third, migrants from the EU27 have become a central – and, in some Nordic countries, dominant – origin group. This shift reflects greater European integration and expanded free movement rights within the European Economic Area, as part of the acceleration of labour market integration processes since the 2004 EU enlargement. By 2024, EU27 migrants made up one of the largest foreign-born groups in all Nordic countries, with particularly high shares in Iceland and Denmark. Migrants from the rest of Europe have also grown, accounting for around one quarter of the foreign-born population in Finland, and between 16 and 19% in Sweden, Norway, and Denmark. Together with EU27 migrants, European-born populations now represent roughly half of the foreign-born population in most Nordic countries, although the share is lower in Sweden. By contrast, the self-governing regions show much smaller rest of Europe shares, as intra-Nordic migration continues to dominate.
Fourth, all Nordic countries demonstrate increasing diversification beyond Europe, with rising shares of migrants from Central and Southern Asia, Eastern and South-Eastern Asia, Sub-Saharan Africa, and Northern Africa and Western Asia. This marks a gradual but steady shift from predominantly European migrant origins toward more globally diverse profiles. The pace and scale of diversification vary across countries, but by 2024 all exhibit a broader mix of non-European migrant groups than in previous decades.
Country group classification
Figure 4.1: Composition of migrant stocks in the Nordic countries. Source: Nordic Statistical Institutes (NSIs). Composition of migrant stocks, 1994-2024. The width of each category is proportional to the absolute size of the migrant group, representing the magnitude of each region within the total migrant population at each time point. Source data: list of specific tables for the NSI’s are available in the references. |
Country-specific migration trajectories
In Denmark, EU27-born migrants have moved from a secondary to a dominant position, accounting for 36% of the foreign-born population by 2024. Rest of Europe remains the second-largest group (19%), while migrants from Central and Southern Asia (11%), Northern Africa and Western Asia (10%), and Eastern and South-Eastern Asia (8%) represent substantial secondary groups. Nordic-born and Sub-Saharan African migrants account for smaller shares. Denmark’s profile reflects continued European predominance alongside a gradual diversification toward Asian and African origins.
Finland exhibits a broadly diversified migrant composition. EU27 and rest of Europe together accounted for 43% in 1994 and 46% in 2024. Since 1994, Finland has experienced pronounced diversification, with migrants from Central and Southern Asia (13%), Eastern and South-Eastern Asia (12%), Sub-Saharan Africa (8%), and Northern Africa and Western Asia (8%) accounting for significant proportions, which demonstrates increasing extra-European representation. Over the same period, Nordic-born migrants declined substantially from 28% to 6.5%.
Iceland presents an overwhelmingly EU27-centric migration profile with limited diversification. The EU27 migrant stock expanded substantially, accounting for 53% of Iceland’s foreign-born population by 2024. Rest of Europe and Nordic migrants each made up approximately 10–11%, while Eastern and South-Eastern Asia accounts for 9%. Other origin groups represent small shares. As such, Iceland’s migration patterns remain more concentrated among European origins than those of other Nordic countries.
Norway is characterised by sustained European predominance alongside substantial extra-European diversification. By 2024, EU27 and rest of Europe had increased to 29% and 17%, respectively, of the foreign-born population, while Nordic migrants declined from 22% to below 9%. However, Nordic migration in Norway has continued to increase in absolute terms, as the total number of Nordic-born population in Norway went from over 50,000 to almost 90,000 in the 1994 to 2024 period. Migrants from Central and Southern Asia, Eastern and South-Eastern Asia, Sub-Saharan Africa, and Northern Africa and Western Asia each make up around 10%. Norway’s profile reflects a combination of strong European migration and broad-based diversification from Asia and Africa.
Sweden has experienced the most substantial shift in migrant composition among the Nordic countries. Between 1990 and 2024, the foreign-born population moved from Nordic predominance to broad diversification. By 2024, migrants from Northern Africa and Western Asia formed the largest group (20%), followed by EU27 (17%) and rest of Europe (16%). Migrants from Central and Southern Asia accounted for 13%. Nordic-born migrants declined from 40% to less than 10%. Despite growing shares of extra-European migrants, European origins remain an important component of Sweden’s migration profile.
The self-governing territories are characterized by a predominance of intra-Nordic foreign-born populations, with some increase in the share of migrants from Eastern and South-Eastern Asia in the Faroe Islands and Greenland, alongside a growing share of EU27 migrants across all three territories. Overall, however, the degree of diversification remains limited relative to that observed in the Nordic countries.
Given the substantial increase in immigration to the Nordic Region over the past three decades and the diversification of migrant populations, the question arises of how this diversity manifests at the local level. The following section examines patterns of diversity by place of birth at the municipal level in 2014 and 2024.
Spatial and temporal patterns of diversity
Maps 4.1 and 4.2 show the Diversity Index for 2014 and 2024 (for details see Box 4.2). Table 4.2 presents the ten most diverse municipalities in each Nordic country. Diversity remains unevenly distributed across the Nordic Region, with clear urban-rural disparities and marked differences between the countries. In 2014, diversity levels were generally modest, with a Nordic regional average of 12.8% and most municipalities below 20%. By 2024, the regional average had increased to 19.3%, reflecting broader diffusion of diversity beyond major urban areas, including parts of northern Sweden and Norway, and parts of Denmark.
Box 4.2: Diversity Index – method and interpretation
The Diversity Index used in this chapter is based on country-of-birth groups and calculated using the Blau Index, DI = 1 - \sum p_i^2 where p_i^{} is the share of each group in the total resident population. In simple terms, the index reflects the probability that two randomly selected individuals in a municipality belong to different origin groups (Rushton, 2008). Higher values indicate higher diversity.
The index is calculated separately for each municipality and year, based on the distribution of the total resident population across origin groups. Group shares are squared and summed, the result of which is then subtracted from 1. When one group dominates, the index approaches zero, while a more even distribution yields higher values closer to 1. The measure is unitless and expressed here as a percentage.
To capture overall diversity, the native-born population must be included. In this chapter, all Nordic countries are grouped together, enabling the index to focus on non-Nordic diversity. This approach reflects shared cultural, economic and social characteristics across the Nordic countries.
The index is sensitive to the number of groups included – in other words, fewer categories produce lower scores. This affects comparability with findings from national statistical institutes that use different groupings, particularly in the self-governing regions, where broader categories mean that diversity values cannot be directly compared to those of Nordic countries. See Appendix 4.2 for more details.
Sweden’s regions and municipalities, particularly those in the south of the country, are the most socially diverse ones across the Nordics, considering the mix of countries of origin of their citizens. The municipal average is around 25%, with considerable variation across the country. By 2024, many municipalities in southern Sweden – including Skåne, Västra Götaland, and the Stockholm metropolitan area – registered index values above 30%, compared with 15–25% in 2014. The most diverse municipalities primarily consist of inner-urban areas in the surroundings of Stockholm, Malmö and Burlöv (see Table 4.2). Although both Malmö and Burlöv have high diversity levels (around 53%), their composition differs: Malmö exhibits overrepresentation across several non-European groups, while Burlöv’s diversity is predominantly European in origin. This highlights how similar index values can reflect different underlying group distributions.
Denmark’s mean diversity level (24%) is similar to that of Sweden, with the highest values concentrated in the Copenhagen metropolitan area. The ten most diverse municipalities in 2024 are all inner-urban areas around Copenhagen, each recording index values above 35% (see Table 4.2). In these municipalities, migrants from rest of Europe and Central and Southern Asia tend to be over-represented. Diversity remains considerably lower in rural North Jutland, where most municipalities fall below 20%. Denmark’s pattern reflects Copenhagen’s role as the country’s primary migration hub, with more limited diversification in its rural regions.
Norway’s diversity increased between 2014 and 2024, reaching a municipal average of 22.6%. Compared with Sweden and Denmark, Norway shows notably lower variation between municipalities, reflecting a more even distribution of migrant groups across the country. Diversity is highest in Oslo and other urban centres along the southern and western coasts, but several rural outliers also stand out. Small northern municipalities such as Træna and Gamvik, as well as rural heartland areas like Frøya and Båtsfjord, register relatively high levels of diversity, driven mainly by EU27 labour migration (see Table 4.2). In contrast, northern inland regions continue to exhibit low diversity levels, typically below 15%. This north–south gradient reflects long-standing settlement patterns, with international migration concentrated in the economically dynamic southern regions, while high levels of diversity in the north are linked to labour demand in specific sectors. This combination of a fairly even national pattern, urban concentration, and distinctive rural outliers distinguishes Norway’s spatial diversity profile from that of its Nordic neighbours.
Finland has a slower and more geographically concentrated diversification trajectory than the other Nordic countries, as reflected in a national Diversity Index of 9% in 2024. Diversity is highest in the Helsinki metropolitan area, where municipalities such as Helsinki, Espoo and Vantaa reach values of 30–40%. Several coastal municipalities, including Närpes and Kaskinen, also show notable levels of diversity due to long-standing labour-market links and sector-specific recruitment (see also Table 4.2). Outside of these areas, diversification has been more limited. Much of central and eastern Finland remains below 15%, illustrating how the later onset of large-scale immigration, together with linguistic and structural factors, has shaped settlement patterns. Overall, Finland exhibits a clear metropolitan–periphery gradient, with a strong concentration of diversity in southern and coastal regions, and comparatively slow diffusion into rural and inland municipalities.
Iceland has undergone one of the most rapid increases in diversity in the Nordic Region. While most municipalities registered Diversity Index values below 15% in 2014, many now fall between 25% and 35%, resulting in the highest national average (25.4%) among the Nordic countries in 2024. This rapid increase is driven primarily by EU27 labour migration. Several rural municipalities now stand out, including Mýrdalshreppur – where EU27-born residents form a majority – along with Bláskógabyggð and Skaftárhreppur. With the exception of Reykjanesbær, Iceland’s most diverse municipalities are rural, reflecting labour shortages in the tourism, service, and resource-based sectors (see Table 4.2). Given the overall rurality of Iceland, it is unsurprising that most of the more diverse municipalities are classified as rural heartland and sparsely populated rural areas. As a result, Iceland’s diversification pattern differs markedly from those of other Nordic countries, where high diversity is more strongly concentrated in metropolitan areas.
In sum, across the Nordic countries, urban areas generally exhibit higher levels of diversity than rural areas, albeit with notable exceptions in Norway and Iceland. Capital cities and major metropolitan areas demonstrate substantially higher diversity levels compared to sparsely populated inland and northern regions. This pattern likely reflects the spatial concentration of the economic opportunities, social networks and institutional infrastructure that facilitate migrant settlement. However, the 2024 map indicate that diversity is no longer confined exclusively to large urban centres. Diversity levels are also increasing in smaller towns and peripheral municipalities, albeit at a slower pace. This spatial diffusion suggests that migration-related demographic change is gradually extending beyond traditional gateway cities, although clear regional disparities persist.
Map 4.1: Nordic Diversity Index by place of birth 2014. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs). The map shows the diversity levels in Nordic countries at the municipal level (national level data for the Faroe Islands). The native and Nordic populations are considered one group, and the remaining countries are grouped according to the methodology applied (see Methodology Box 4.2 and Appendix 4.1). Nordic average: 12.8 |
See and download map in |
Map 4.2: Nordic Diversity Index by place of birth 2024. Source: Nordregio calculations based on data from National Statistical Institutes (NSIs). The map shows the diversity levels in Nordic countries at the municipal level (national level data for the Faroe Islands). The native and Nordic populations are considered one group, and the remaining countries are grouped according to the methodology applied (see Methodology Box 4.2 and Appendix 4.1). Nordic average: 19.3 |
See and download map in |
Discussion: Shared trajectories, distinct pathways
The Nordic countries show broadly similar migration trajectories over the last 30 years, characterised by declining intra-Nordic migration, increasing EU27 representation and growing shares of migrants from Asia and Africa. At the same time, each country displays distinct profiles shaped by national labour markets, migration histories, policy frameworks and institutional contexts. Spatial analysis highlights that national compositions mask substantial variation within each country, with migrant groups distributed unevenly across municipalities and regions.
The analysis reveals three key insights into how migration and diversity are reshaping the Nordic region. First, the Nordics have recently transitioned from a predominantly regional to a more global migration system. While the timing differs across countries – Sweden has transitioned the fastest, Finland more gradually – there is an overall shift from declining intra-Nordic migration, to increasing European origins and growing shares of migrants from Asia and Africa.
Second, diversity is subject to spatial diffusion. Metropolitan areas remain the primary destinations for migrants, but increasing levels of diversity in smaller towns and peripheral municipalities show that migration-driven change is no longer confined to urban centres. Norway’s rural EU27 outliers and Iceland’s widespread rural diversification illustrate how labour demand outside major cities can shape local diversity patterns.
Third, differentiated yet interconnected migration patterns create both shared challenges and opportunities. All of the Nordic countries face similar demographic pressures, including ageing populations and the need for labour force renewal, but their experiences differ in terms of migration composition and pace of diversification. This variation opens up space for mutual learning on integration, labour market inclusion and the design of services that meet the needs of increasingly diverse populations. While integration challenges may manifest differently across countries and between municipality types, they are no longer confined to urban centres.
Although intra-Nordic migration has declined, growing diversity across the whole region offers new grounds for co-operation. Due to their strong institutional frameworks, inclusive welfare systems and emphasis on intergovernmental collaboration, the Nordic countries are in a good position to manage demographic change while maintaining social cohesion across a widening range of local contexts.
Table 4.2: Top 10 most diverse municipalities by country, 2014 including Nordic Urban-Rural Typology classification (Stjernberg et al. 2024) and minority group distribution. Source: Nordic Statistical Institutes (NSIs). Grey background indicates that a minority group is over-represented in a municipality compared to their share in the national population, defined by two DS above the mean. Data for the Faroe Islands, Greenland and Åland are excluded due to comparability constraints, as the diversity index is sensitive to the number of categorical groups employed in its calculation. Abbreviations are: native population (NAT) or Nordic countries (NORD), EU27 (excluding Nordic countries) (EU), Rest of Europe (excluding EU and Nordic countries) (EUR), Northern Africa and Western Asia (NAWA), Sub-Saharan Africa (SSA), Eastern and South-Eastern Asia (ESEA), Central and Southern Asia (CSA), Northern America (NA), Latin America and the Caribbean (LAC), Oceania (OCE), Stateless/Unknown (UNK), Other countries (OTH). Note: List of specific tables for data sources from the NSIs are available in the references |
Denmark
Municipality name | Nordic urban-rural typology classification | TOTAL POP | DI | NAT/ NORD | EU27 | EUR | NAWA | SSA | ESEA | CSA | NA | LAC | OCE | UNK/ Other |
Ishøj | Inner urban area | 23,663 | 63.79 | 54.83 | 6.86 | 19.07 | 4.42 | 1.61 | 1.55 | 13.38 | 0.06 | 0.31 | 0.03 | 0.00 |
Brøndby | Inner urban area | 39,067 | 58.54 | 61.27 | 6.27 | 13.16 | 5.97 | 1.79 | 1.89 | 11.69 | 0.16 | 0.60 | 0.04 | 0.02 |
Høje-Taastrup | Inner urban area | 57,540 | 55.61 | 64.05 | 8.73 | 11.35 | 3.87 | 1.74 | 1.68 | 10.52 | 0.17 | 0.38 | 0.03 | 0.03 |
Vallensbæk | Inner urban area | 17,800 | 52.53 | 66.67 | 5.94 | 10.83 | 2.96 | 1.10 | 2.01 | 11.64 | 0.17 | 0.63 | 0.03 | 0.01 |
Albertslund | Inner urban area | 27,677 | 51.32 | 67.59 | 3.53 | 12.41 | 3.71 | 1.19 | 1.02 | 10.84 | 0.18 | 0.55 | 0.02 | 0.00 |
Rødovre | Inner urban area | 44,328 | 43.74 | 74.10 | 4.53 | 6.27 | 3.85 | 1.75 | 2.00 | 7.30 | 0.16 | 0.71 | 0.03 | 0.02 |
København | Inner urban area | 659,350 | 43.29 | 74.50 | 7.65 | 4.61 | 3.64 | 1.79 | 2.11 | 4.07 | 1.06 | 1.56 | 0.24 | 0.02 |
Gladsaxe | Inner urban area | 70,600 | 42.61 | 74.90 | 4.78 | 4.94 | 3.07 | 1.90 | 1.83 | 8.09 | 0.25 | 0.79 | 0.06 | 0.03 |
Glostrup | Inner urban area | 23,655 | 40.90 | 75.84 | 4.71 | 7.08 | 3.88 | 1.07 | 1.96 | 8.09 | 0.14 | 0.47 | 0.04 | 0.02 |
Herlev | Inner urban area | 29,876 | 39.25 | 77.14 | 3.22 | 7.50 | 3.51 | 1.46 | 1.70 | 6.39 | 0.15 | 0.54 | 0.04 | 0.01 |
Mean across municipalities | 24.11 | 86.49 | 4.02 | 3.48 | 1.88 | 0.72 | 1.06 | 2.08 | 0.17 | 0.28 | 0.04 | 0.00 | ||
Standard deviation across municipalities | 10.55 | 7.15 | 1.74 | 2.74 | 1.05 | 0.42 | 0.52 | 2.72 | 0.17 | 0.23 | 0.04 | 0.01 |
Finland
Municipality name | Nordic urban-rural typology classification | TOTAL POP | DI | NAT/ NORD | EU27 | EUR | NAWA | SSA | ESEA | CSA | NA | LAC | OCE | UNK/ Other |
Vantaa | Inner urban area | 251,269 | 41.31 | 75.94 | 4.84 | 6.70 | 2.51 | 0.89 | 2.54 | 3.79 | 0.15 | 0.29 | 0.04 | 2.24 |
Espoo | Inner urban area | 320,931 | 37.60 | 78.51 | 3.56 | 5.14 | 2.04 | 0.67 | 3.19 | 4.24 | 0.36 | 0.45 | 0.06 | 1.74 |
Närpes | Rural heartland | 9,554 | 33.80 | 80.48 | 1.27 | 5.71 | 0.00 | 0.42 | 10.41 | 0.00 | 0.23 | 0.18 | 0.00 | 0.10 |
Helsinki | Inner urban area | 684,018 | 31.70 | 82.35 | 3.28 | 4.19 | 1.72 | 0.51 | 2.12 | 2.63 | 0.42 | 0.48 | 0.08 | 2.20 |
Kaskinen | Rural heartland | 1,241 | 28.55 | 84.29 | 0.00 | 3.71 | 0.00 | 0.00 | 5.16 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Kerava | Inner urban area | 38,461 | 27.12 | 85.13 | 4.26 | 4.13 | 1.14 | 0.51 | 1.38 | 1.42 | 0.12 | 0.13 | 0.04 | 1.21 |
Jakobstad | Inner urban area | 19,576 | 26.99 | 85.24 | 1.64 | 4.43 | 1.37 | 0.37 | 1.76 | 2.57 | 0.11 | 0.12 | 0.00 | 1.33 |
Turku | Inner urban area | 206,073 | 26.42 | 85.58 | 2.41 | 3.90 | 1.94 | 0.59 | 1.40 | 2.40 | 0.17 | 0.27 | 0.03 | 1.22 |
Vaasa | Inner urban area | 70,361 | 23.66 | 87.22 | 1.34 | 2.63 | 1.02 | 0.26 | 1.93 | 3.06 | 0.13 | 0.21 | 0.00 | 1.79 |
Mean across municipalities | 9.28 | 95.04 | 0.71 | 1.70 | 0.14 | 0.11 | 0.47 | 0.19 | 0.03 | 0.02 | 0.00 | 0.11 | ||
Standard deviation across municipalities | 6.09 | 3.28 | 1.06 | 1.49 | 0.33 | 0.17 | 0.82 | 0.54 | 0.07 | 0.06 | 0.01 | 0.32 |
Iceland
Municipality name | Nordic urban-rural typology classification | TOTAL POP | DI | NAT/ NORD | EU27 | EUR | NAWA | SSA | ESEA | CSA | NA | LAC | OCE | UNK/ Other |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mýrdalshreppur | Sparsely populated rural area | 881 | 52.01 | 42.45 | 54.71 | 1.93 | 0.00 | 0.11 | 0.23 | 0.00 | 0.34 | 0.11 | 0.00 | 0.11 |
Skaftárhreppur | Sparsely populated rural area | 620 | 47.52 | 63.06 | 35.65 | 0.32 | 0.00 | 0.00 | 0.32 | 0.16 | 0.00 | 0.48 | 0.00 | 0.00 |
Súðavíkurhreppur | Sparsely populated rural area | 219 | 45.96 | 71.69 | 14.16 | 5.02 | 5.94 | 0.00 | 0.46 | 0.46 | 0.46 | 0.00 | 0.00 | 1.83 |
Bláskógabyggð | Sparsely populated rural area | 1,322 | 45.24 | 67.17 | 31.01 | 1.29 | 0.15 | 0.00 | 0.08 | 0.00 | 0.15 | 0.15 | 0.00 | 0.00 |
Reykjanesbær | Local centre in rural area | 21,957 | 44.51 | 70.61 | 23.56 | 2.22 | 1.01 | 0.03 | 0.77 | 0.21 | 0.21 | 0.90 | 0.03 | 0.44 |
Hrunamanna-hreppur | Rural heartland | 865 | 42.22 | 70.98 | 27.17 | 1.16 | 0.00 | 0.00 | 0.58 | 0.12 | 0.00 | 0.00 | 0.00 | 0.00 |
Vesturbyggð | Sparsely populated rural area | 1,356 | 40.42 | 73.53 | 23.45 | 1.11 | 0.07 | 0.00 | 0.66 | 0.07 | 0.29 | 0.59 | 0.00 | 0.22 |
Sveitarfélagið Hornafjörður | Sparsely populated rural area | 2,487 | 40.03 | 73.90 | 23.08 | 1.21 | 0.12 | 0.04 | 0.88 | 0.20 | 0.08 | 0.24 | 0.04 | 0.20 |
Rangárþing eystra | Rural heartland | 2,007 | 39.18 | 74.19 | 24.02 | 1.15 | 0.05 | 0.00 | 0.05 | 0.20 | 0.00 | 0.30 | 0.05 | 0.00 |
Grundar-fjarðarbær | Sparsely populated rural area | 821 | 38.01 | 75.64 | 21.80 | 1.22 | 0.00 | 0.00 | 0.00 | 0.12 | 0.00 | 0.73 | 0.24 | 0.24 |
Mean across municipalities | 25.38 | 84.31 | 13.26 | 1.10 | 0.27 | 0.01 | 0.40 | 0.09 | 0.19 | 0.21 | 0.02 | 0.16 | ||
Standard deviation across municipalities | 11.60 | 9.59 | 9.08 | 1.26 | 0.79 | 0.02 | 0.44 | 0.14 | 0.23 | 0.23 | 0.05 | 0.27 |
Norway
Municipality name | Nordic urban-rural typology classification | TOTAL POP | DI | NAT/ NORD | EU27 | EUR | NAWA | SSA | ESEA | CSA | NA | LAC | OCE | UNK/ Other |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Træna | Sparsely populated rural area | 442 | 44.01 | 72.17 | 18.78 | 5.20 | 0.00 | 0.00 | 3.17 | 0.68 | 0.00 | 0.00 | 0.00 | 0.00 |
Lørenskog | Inner urban area | 48,188 | 43.67 | 74.21 | 6.54 | 4.86 | 2.00 | 2.50 | 2.70 | 6.50 | 0.15 | 0.51 | 0.03 | 0.00 |
Frøya | Rural heartland | 5,453 | 42.59 | 72.80 | 20.76 | 2.13 | 0.68 | 1.17 | 1.78 | 0.17 | 0.07 | 0.44 | 0.00 | 0.00 |
Ullensaker | Periurban area | 43,814 | 41.95 | 75.36 | 8.58 | 3.62 | 2.49 | 2.41 | 2.00 | 4.77 | 0.13 | 0.61 | 0.04 | 0.00 |
Båtsfjord | Rural heartland | 2,113 | 41.70 | 74.40 | 16.47 | 4.35 | 0.99 | 0.71 | 1.37 | 1.28 | 0.00 | 0.43 | 0.00 | 0.00 |
Oslo municipality | Inner urban area | 717,710 | 41.51 | 75.85 | 5.94 | 3.80 | 2.49 | 3.18 | 2.32 | 4.79 | 0.47 | 1.06 | 0.10 | 0.00 |
Gamvik | Sparsely populated rural area | 1,070 | 39.71 | 75.98 | 14.86 | 5.51 | 0.93 | 0.56 | 1.87 | 0.00 | 0.28 | 0.00 | 0.00 | 0.00 |
Lillestrøm | Inner urban area | 94,201 | 39.06 | 77.43 | 7.07 | 2.93 | 2.75 | 1.87 | 2.47 | 4.82 | 0.18 | 0.45 | 0.03 | 0.00 |
Drammen | Inner urban area | 104,487 | 38.63 | 77.74 | 6.51 | 4.61 | 2.63 | 2.51 | 1.78 | 3.52 | 0.17 | 0.51 | 0.03 | 0.00 |
Rælingen | Inner urban area | 20,099 | 37.47 | 78.50 | 6.91 | 2.97 | 2.28 | 2.01 | 2.10 | 4.49 | 0.14 | 0.58 | 0.02 | 0.00 |
Mean across municipalities | 22.64 | 87.57 | 4.91 | 3.30 | 1.06 | 0.94 | 1.13 | 0.65 | 0.14 | 0.27 | 0.03 | 0.00 | ||
Standard deviation across municipalities | 6.40 | 3.96 | 2.83 | 1.54 | 0.68 | 0.65 | 0.58 | 0.80 | 0.13 | 0.22 | 0.06 | 0.00 |
Sweden
Municipality name | Nordic urban-rural typology classification | TOTAL POP | DI | NAT/ NORD | EU27 | EUR | NAWA | SSA | ESEA | CSA | NA | LAC | OCE | UNK/ Other |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Botkyrka | Inner urban area | 95,905 | 62.99 | 58.20 | 6.33 | 9.89 | 9.78 | 3.81 | 1.57 | 7.59 | 0.16 | 2.46 | 0.04 | 0.16 |
Södertälje | Inner urban area | 102,911 | 60.95 | 58.75 | 6.37 | 5.85 | 18.62 | 2.71 | 1.07 | 4.57 | 0.16 | 1.73 | 0.02 | 0.16 |
Sigtuna | Inner urban area | 52,767 | 57.39 | 63.57 | 6.14 | 4.32 | 7.27 | 5.57 | 2.29 | 8.40 | 0.31 | 1.78 | 0.06 | 0.28 |
Järfälla | Inner urban area | 88,950 | 54.94 | 65.60 | 4.67 | 4.56 | 8.38 | 4.11 | 2.18 | 7.98 | 0.29 | 2.02 | 0.04 | 0.17 |
Burlöv | Inner urban area | 20,101 | 53.28 | 66.70 | 7.29 | 9.56 | 7.60 | 1.14 | 1.75 | 3.75 | 0.22 | 0.71 | 0.00 | 1.27 |
Malmö | Inner urban area | 365,644 | 53.27 | 66.86 | 6.13 | 7.59 | 8.59 | 1.94 | 1.90 | 4.97 | 0.49 | 1.37 | 0.10 | 0.06 |
Upplands Väsby | Inner urban area | 50,323 | 52.12 | 67.91 | 5.23 | 4.46 | 6.03 | 2.85 | 2.28 | 8.71 | 0.24 | 1.93 | 0.02 | 0.33 |
Solna | Inner urban area | 85,789 | 52.06 | 67.97 | 6.89 | 4.67 | 2.81 | 1.74 | 4.77 | 8.19 | 0.59 | 2.05 | 0.11 | 0.22 |
Sundbyberg | Inner urban area | 56,274 | 51.88 | 68.26 | 5.47 | 4.41 | 5.58 | 4.74 | 2.31 | 6.38 | 0.45 | 2.02 | 0.09 | 0.29 |
Upplands-Bro | Periurban area | 32,868 | 50.87 | 68.96 | 5.31 | 3.72 | 6.44 | 4.81 | 1.34 | 6.69 | 0.18 | 1.80 | 0.04 | 0.71 |
Mean across municipalities | 24.97 | 86.13 | 3.06 | 2.19 | 2.97 | 1.18 | 1.04 | 1.42 | 0.15 | 0.36 | 0.02 | 1.46 | ||
Standard deviation across municipalities | 9.71 | 6.26 | 1.70 | 1.58 | 2.31 | 1.05 | 0.61 | 1.44 | 0.13 | 0.48 | 0.03 | 0.99 |
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Appendix 4.1. Country groups classification adopted in the minority calculations
The classification is based on the final year of observation (2024), such that countries are assigned to consistent categories across all time points. For example, Bulgaria, Romania, and Croatia were classified as EU27 members in 1994, despite not having acceded to the European Union at that time. .
The classification is based on the final year of observation (2024), such that countries are assigned to consistent categories across all time points. For example, Bulgaria, Romania, and Croatia were classified as EU27 members in 1994, despite not having acceded to the European Union at that time.
Denmark
Faroe Islands
Finland
Greenland
Iceland
Norway
Sweden
EU27
Austria
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Estonia
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Austria
Belgium
Bulgaria
Croatia
Cyprus
Czech Republic
Estonia
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Albania
Andorra
Belarus
Bosnia-Herzegovina
Gibraltar
Guernsey
Isle of Man
Jersey
Kosovo
Liechtenstein
Moldova
Monaco
Montenegro
North Macedonia
Russia
San Marino
Serbia
Serbia and Montenegro
Switzerland
Türkiye
Ukraine
United Kingdom
Vatican City State
Afghanistan
Armenia
Azerbaijan
Bangladesh
Bhutan
Georgia
India
Iran
Kazakhstan
Kyrgyzstan
Maldives
Nepal
Pakistan
Sri Lanka
Tajikistan
Turkmenistan
Uzbekistan
Brunei
Cambodia
China
Hong Kong
Indonesia
Japan
Laos
Macao
Malaysia
Mongolia
Myanmar
North Korea
Philippines
Singapore
South Korea
Taiwan
Thailand
Timor-Leste
Vietnam
Anguilla
Antigua and Barbuda
Argentina
Aruba
Bahamas
Barbados
Belize
Bolivia
Bonaire, Sint Eustatius and Saba
Brazil
British Virgin Islands
Cayman Islands
Chile
Colombia
Costa Rica
Cuba
Curaçao
Dominica
Dominican Republic
Ecuador
El Salvador
Falkland Islands
Franske Antiller
French Guiana
Grenada
Guadeloupe
Guatemala
Guyana
Haiti
Honduras
Jamaica
Martinique
Mexico
Montserrat
Netherlands Antilles
Nicaragua
Panama
Paraguay
Peru
Puerto Rico
Saint Barthélemy
Saint Martin
Saint Kitts and Nevis
Saint Lucia
Saint Pierre and Miquelon
Saint Vincent and the Grenadines
Sint Maarten
Suriname
Trinidad and Tobago
Turks and Caicos Islands
United States Virgin Islands
Uruguay
Venezuela
Algeria
Bahrain
Egypt
Iraq
Israel
Jordan
Kuwait
Lebanon
Libya
Morocco
Oman
Palestine
Qatar
Saudi Arabia
Sudan
Syria
Tunisia
United Arab Emirates
Western Sahara
Yemen
Bermuda
Canada
United States
American Samoa
Australia
Christmas Island
Cocos (Keeling) Islands
Cook Islands
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Micronesia, Federated States
Nauru
New Caledonia
New Zealand
Niue
Norfolk Island
Northern Mariana Islands
Palau
Papua New Guinea
Pitcairn
Samoa
Solomon Islands
Tokelau
Tonga
Tuvalu
United States Minor Outlying Islands
Vanuatu
Wallis and Futuna Islands
Angola
Benin
Botswana
British Indian Ocean Territory
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo-Brazzaville
Côte d'Ivore
Djibouti
Equatorial Guinea
Eritrea
Eswatini
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Mozambique
Namibia
Niger
Nigeria
Réunion
Rwanda
Saint Helena
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
South Sudan
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Appendix 4.2. Greenland and Åland country group classification at subnational level, as adopted for the population diversity maps.
Native population
Nordic countries
Faroe Islands
Denmark
Nordics
Europe
Rest of Europe
Africa
America
Asia
Oceania
Faroe Islands
Denmark
Nordics
Europe
Rest of Europe
Africa
America
Asia
Oceania
Native population
Nordic countries
Finland
Sweden
Other countries
Other including 'unknown'