Maps

The map presents a typology of internal net migration by considering average annual internal net migration in 2020-2021 alongside the same figure for 2018-2019. The colours on the map correspond to six possible migration trajectories: Dark blue: Internal net in migration as an acceleration of an existing trend (net in-migration in 2020-2021 + increase compared to 2018-2019) Light blue: Internal net in migration but at a slower rate than previously (net in-migration in 2020-2021 + decrease compared to 2018-2019) Green: Internal net in migration as a new trend (net in-migration in 2020-2021 + change from net out-migration compared to 2018-2019) Yellow: Internal net out migration as a new trend (net out-migration in 2020-2021 + change from net in-migration compared to 2018-2019) Orange: Internal net out migration but at a slower rate than previously (net out-migration in 2020-2021 + decrease compared to 2018-2019) Red: Internal net out migration as a continuation of an existing trend (net out-migration in 2020-2021 + increase compared to 2018-2019) The patterns shown around the larger cities reinforces the message of increased suburbanisation as well as growth in smaller cities in proximity to large ones. In addition, the map shows that this is in many cases an accelerated (dark blue circles), or even new development (green circles). Interestingly, although accelerated by the pandemic, internal out migration from the capitals and other large cities was an existing trend. Helsinki stands out as an exception in this regard, having gone from positive to negative internal net migration (yellow circles). Similarly, slower rates of in migration are evident in the two next largest Finnish cities, Tampere and Turku (light blue circles). Akureyri (Iceland) provides an interesting example of an intermediate city which began to attract residents during the pandemic despite experiencing internal outmigration prior. From a rural perspective there are…

The map shows the average internal net migration in 2020 and 2021 for Nordic municipalities. Blue dots indicate positive internal net migration (more people moving in than out) and red dots indicate negative internal net migration (more people moving out than in), while the size of the dots represents the extent of the positive or negative trend. Internal migration refers to a change of address within the same country. The map shows substantial outmigration from the Nordic capitals, as well as from Gothenburg and Malmö in Sweden. Alongside increased suburbanisation, the map also provides some evidence of growth in medium-sized cities and smaller cities within commuting distance of larger cities.    

The map shows the relative change of the OECD House Price Index from Q2 2020 to Q2 2021. The map shows that the price development was not uniform within the countries. Iceland recorded the largest price increases overall, with the most marked price increases found outside of the capital region. All Swedish regions recorded increases above 20%, with the highest increases in the Stockholm and Malmö regions. All Norwegian regions showed price increases, though to a lesser extent than Swedish regions in most cases. In Denmark, Bornholm, Sjælland and the rural islands of Lolland and Falster recorded relatively high price increases, although many rural areas developed from low absolute prices in 2020. Finland was the only country where some regions saw property prices decrease. Moderate increases were still observed in some of the southern regions, where the major cities are located, and in the north.

The map shows the contraction of national economies in Western Europe in 2020. In 2020, the global economy contracted by -3.1%. Nonetheless, the economic trajectories of the various regions have been very different. While emerging markets shrank by -2.1%, advanced economies declined by twice that rate (-4.5%). The European Union was one of the regions more severely impacted by the Covid-19 crisis. Here, the GDP contraction in 2020 was -5.9% (-6.4% in the Euro area). The economic repercussions of the pandemic in the Nordic countries were less severe. On average, the Nordic economies contracted by -3.0% in 2020. Even though all countries in the Nordic Region experienced an economic deceleration, the impacts were very different in different areas. Measured in real terms, in 2020, GDP volumes shrunk between -7.1% in Iceland and -0.7% in Norway. Somewhere in the middle were Denmark (-2.1%), Finland (-2.3%), and Sweden (-2.9 %). In the autonomous territories, the GDP contraction ranged from a -2.8% decline in the Faroe Islands to a modest 0.4% expansion in Greenland.

The map shows changes in number of people travelling to work in the Nordic municipalities with the biggest populations comparing November 2021 to a pre-pandemic baseline. The map compares the average number of people who travelled to work on a weekday in January and February 2020 with the number of people who travelled to work on 11 November 2021 in the ten largest cities of Denmark, Finland, Norway and Sweden. The date was selected as the reference date as it is considered to be a relatively typical Thursday. It also represents a point when few movement restrictions were in place in the Nordic countries. As can be seen from the map, all of the municipalities highlighted recorded a fall in work-related travel on 11 November compared to the pre-pandemic baseline. It was biggest in Stavanger (-36%), followed by Stockholm (-31%), Oulu (-30%), Bærum (adjacent to Oslo) (-29%), Frederiksberg (adjacent to Copenhagen) (-29%) and Helsinki (-29%). In general, the decrease was highest around the capital regions and larger cities, but there were exceptions, for example, Jyväskylä (-26%), Örebro (-25%), Jönköping (-21%), and Randers (-20%). Several large municipalities also stood out because their patterns did not change so much, for example, Helsingborg (-3%) and Västerås (-7%) in Sweden; Viborg (-3%) and Odense (-8%) in Denmark. 

The map shows the share of jobs that can potentially be done from home. At the municipal level it shows that the highest proportion is in, or in the proximity of, the largest urban conurbations.   The purple areas show the municipalities that has a remote-work potential above the Nordic average (37%) and the blue areas the municipalities with remote-work potential below the Nordic average.  The indicator is based on the methodology of Dingel & Neiman (2020). This method estimates the proportion of jobs that can theoretically be performed from home based on the tasks included in different occupations. Dingel & Neiman’s US classification was translated to the European International Standard Classification of Operations (ISCO-08) codes. The data is based on the 4-digit ISCO-08 and includes 437 occupations. The result was that every 4-digit ISCO occupation was coded as either 1: possible to work from home or not possible to work from home. For more information about the method please look at the State of the Nordic Region 2022 publication.  The ten municipalities with the highest proportions are all in capital regions, with seven out of 10 in either Copenhagen (Hovedstaden) or Stockholm Region. In general, people in urban municipalities are more likely to be able to work from home (46.2%) than those in intermediate municipalities (32.3%) and rural municipalities (27.8%).    It seems to be the case that the higher proportion of jobs that can be done from home in urban areas relates to the differences in industrial and occupational profiles between urban and rural areas, in particular, a higher concentration of knowledge-intensive occupations in urban areas. These differences are also evident when comparing countries. For example, Denmark has a rather large number of municipalities with high proportions of jobs that can be done from home. This may be due to…

The map shows the largest Nordic-born minority group at the municipal level among the working-age population (15-64 years old). The intensity of the colour shows the share of the total foreign-born Nordic population, with darker tints indicating a larger percentage than lighter tints. The map illustrates differences at the regional and municipal levels within the countries. For example, while the largest minority in Norway are born in Sweden, those born in Denmark constitute the largest minority Nordic-born group in the southern Norwegian region of Agder. The largest Nordic-born minority in Denmark are those born in Sweden in absolute numbers and in the capital region of Hovedstaden, while the largest minority in all other Danish regions is from Greenland. In Sweden, the largest Nordic-born minority overall are from Finland, but there are also regional differences here: in the regions of Skåne, Halland and Kronoberg, the largest Nordic minority group come from Denmark, and in Värmland and Jämtland-Härjedalen, the largest is Norwegian born. In the cross-border municipalities, this pattern is even more accentuated and made evident in areas such as Haparanda in Sweden (the twin city of Tornio in Finland) where 26.5% of the population is Finnish born. Åland has the highest share of other Nordic nationals, where, for example, 47% of the population in the municipality of Kökar is born in a different Nordic country (including Finnish born).  Excluding the municipalities of Åland, Haparanda is the municipality in which Nordic-born minorities make up the highest percentage of the total working-age population.

The map shows a typology of European regions by combining information on pre-pandemic unemployment rates with unemployment rates in 2020, based on the annual Labour Force Survey (LFS) that is measured in November. On one axis, the typology considers the extent of the change in the unemployment rate between 2019 and 2020. On the other axis, it considers whether the unemployment rate in 2020 was above or below the EU average of 7.3%. Regions are divided into four types based on whether the unemployment rate decreased or increased and how it relates to the EU average. Regions falling into the first type, shown in red on the map, had an increase in the unemployment rate in 2020 as well as an above-average unemployment rate in general in 2020. These regions were most affected by the pandemic. They are mainly found in northern and central parts of Finland, southern and eastern Sweden, the capital area of Iceland, Latvia, Lithuania, Spain and central parts of France. Regions falling into the second type, shown in orange on the map, had an increase in the unemployment rate in 2020 but a below-average unemployment rate in general in 2020. These regions had low pre-pandemic unemployment rates and so were not as badly affected as the red regions, despite the rising unemployment rates. They are located in Denmark, Iceland, Norway, Åland, southern and western Finland, Sweden (Gotland, Jönköping, and Norrbotten), Estonia, Ireland, northern Portugal and central and eastern parts of Europe.

The map shows the natural population change in the Nordic Region from January to September 2021 While all Nordic countries except Finland were characterised by positive natural population change during 2021, this growth was often particularly pronounced in and around cities and towns, with their relatively youthful populations. Urban centres and their surrounding areas such as Stockholm and Malmö in Sweden, Oslo and Trondheim in Norway, Espoo and Helsinki in Finland or Aarhus and Copenhagen in Denmark all reported among the highest rates of natural population growth during the first nine months of 2021. Rural regions with their often-older population age structures were more likely to experience natural population decline, a pattern that had already existed prior to the pandemic. Especially in Finland, many rural municipalities reported high natural population decline during the first nine months of 2021, despite increases in the number of births, as shown in the map “Change in the number of births in the Nordics”. In the other Nordic countries, only a few municipalities experienced similarly high levels of natural population decline. 

The map shows percentage change in the number of live births in Nordic regions, comparing January-September 2021 to the same period in 2020. While most Nordic countries and autonomous territories saw a rise in births during the pandemic, not all regions followed this trend to the same extent. Rural regions stand out as having had both baby booms and baby busts during the pandemic. In Finland, for example, rural regions reported both large increases in births (Kainuu) but also declines (Central Ostrobothnia). In Sweden, only a few regions registered an increase in the number of babies conceived during the pandemic; among those were rural Gotland and Jämtland. Kronoberg and Dalarna, by contrast, reported a drop of more than 3% in the number of births.

The map shows the number of births during the first nine months of 2021 (January to September) compared to the number of births during the same months in 2020. The babies born during the first nine months of 2021 were conceived between the spring and winter of 2020 when the first waves of the pandemic affected Europe. Babies born during the first nine months of 2020 were conceived in 2019 (i.e., before the pandemic). The map therefore compares the number of births conceived before and during the pandemic. At the time of writing, it seems as if both baby boom and baby bust predictions have been correct, with developments playing out differently across countries. In many Southern and Eastern European countries, such as Spain, Italy or Romania, the number of births declined by more than 1% during the first nine months of 2021. In Portugal and Poland, but also Greenland, drops in the number of births were particularly sharp with more than 5% fewer babies born in 2021. In several of these “baby bust” countries, these decreases in fertility came on top of already low fertility rates. Spain, Italy, Portugal and Poland, for instance, all already had a total fertility rate (TFR) of less than 1.5 children per woman before the crisis. These values are substantially below the so-called ‘replacement ratio’ of 2.1 children per woman, which is necessary to maintain population size. In these countries, existing demographic challenges have thus been aggravated during the pandemic.

The excess mortality has affected overall life expectancy at birth across Europe. In 2019, prior to the start of the pandemic, Spain, Switzerland, and Italy had the highest life expectancy in Europe, followed closely by Sweden, Iceland, France, and Norway. Finland and Denmark had slightly lower levels but were still at or above the EU average (Eurostat, 2021). Life expectancy across the EU as a whole and in nearly all other countries has been steadily increasing for decades. Declines in life expectancy are rare, but that is indeed what happened in many countries in Europe during the pandemic in 2020. One study of upper-middle and high-income countries showed that life expectancy declined in 31 of 37 countries in 2020. The only countries where life expectancy did not decline were New Zealand, Taiwan, Iceland, South Korea, Denmark and Norway. The largest falls were in Russia and the United States. The high excess mortality in Sweden in 2020 has had an impact on life expectancy. In Iceland, Norway, Finland, Denmark and the Faroe Islands, life expectancy went up for both sexes in 2020 (data not yet available for Greenland and Åland). In Sweden, life expectancy fell by 0.7 years for males from 81.3 years to 80.6 and for females by 0.4 years from 84.7 to 84.3 years. The steeper decline in life expectancy for males is consistent with the larger number of excess deaths among males.  Thus, compared to other Nordic countries, the adverse mortality impact of the pandemic has been greater in Sweden. However, when comparing Sweden to the rest of Europe, it is the Nordic countries, other than Sweden, which are exceptional. The trend among countries in Europe is for a fall in life expectancy in 2020. The largest declines were in countries in southern and eastern Europe. Italy and…

The map shows the percentage change for work mobility in Nordic regions and municipalities compared to a pre-pandemic baseline.  The maps compare Nordic mobility patterns in each quarter of 2020 and 2021 with a pre-pandemic baseline. Based on Google data, the panels illustrate the impact of national restrictions and how those restrictions hampered work mobility. As the restrictions were both national and regional in nature, some regions and municipalities were more affected than others.  The darker areas in the map show that work mobility decreased the most Q2 and Q3 2020 and in Q3 2021. The panels also show that mobility decreased later in Sweden than in the other Nordic countries. However, the decrease in Q3 in both 2020 and 2021 may partly be explained by the summer vacation months, when work mobility tends to decrease anyway. In Q4 2021, the overall situation seems to improve, although the pattern is mixed. In a few municipalities the situation is almost back to pre-pandemic baseline while in most municipalities, there is still less mobility in the labour market compared to the pre-pandemic situation.

The map shows the relative change in single-family house prices from fourth quarter 2019 and first quarter 2020 to second and third quarter of 2021 for Danish municipalities. The map shows that most municipalities experienced high price increases during this period, but the extent to which this change was more pronounced differs between municipalities. The increases are highest in the Copenhagen Region, but also in Aarhus and surrounding municipalities, and the peri-urban areas around Vejle. High relative increases are also found in coastal and island municipalities (e.g.  Bornholm, Lolland, Svendborg and around the Western part of the Limfjord), though it is worth noting that these municipalities had lower relative house prices to begin with. These patterns may reflect changing preferences for houses, demand for more space and access to recreational areas.

The map shows the cumulative number of confirmed cases of Covid-19 per 10,000 inhabitants until 31 December 2021 in most European countries. In a European context, the Nordic countries stand out as having a relatively low number of confirmed cases in most regions. Sweden and Denmark stand out as the countries with the highest number of confirmed cases among the Nordic countries. It is possible however that the high case numbers in Denmark can be explained, at least to some extent, by the high levels of testing per capita. Looking at Europe as a whole, the UK, Spain, Czechia, Croatia and the Baltic States stand out as having experienced the highest number of cases overall. From a regional perspective, higher case numbers can often be observed around the capital regions and other large cities. There are also several cross-border differences that are interesting to note. In Germany, for example, the border with the Netherlands is clearly evident with a much higher number of confirmed cases on the Dutch side of the border. In contrast, the situation in the eastern part of the country more closely resembles that in the Czechia than what was observed in the rest of Germany. 

Help Santa! To reduce his transit times and emissions – reindeers burn a lot of (green) fuel – and find an optimal remote workplace from where to deliver gifts to all the children in the Nordic Region! Santa has heard about this new trend “multilocational lifestyle” and he would like to know if this would suit him as well. But where to move? Santa’s little researchers have worked hard this year and done some mapping for him – and discovered places you have never even heard of! If Santa is to serve all children (0-14 years old) throughout the Nordic Region from a single address, the solution lies in Storfors Municipality. WHERE? – you might think. It is a real place, in Central-Southern Sweden. Here Santa has an average distance of 425 km distance to each child from his own backyard. This still sounds like awfully many kilometers. Could he be even more multilocal – with a home in each of the Nordic countries? This would help him to reduce his overall commuting to work significantly. Let’s try it! If he serves all 4.974 children in Åland from a residence (like a luxury hotel with all-inclusive and pets allowed) in Jomala Municipality, he will only have to travel 11 km to work on average. In Greenland, the distances are somewhat larger, and Santa, even with the most optimal location from a residence (a cabin) in Qeqqata Municipality would have to travel 288 km to each of the 11,748 children in the country. Can you guess what the other optimal locations would be in the Nordics? I bet you can’t so I will tell you: it’s the municipalities of Hallsberg in Sweden, Jämsä in Finland, Etnedal in Norway, Kalundborg in Denmark, Kjósarhreppur in Iceland and Tórshavn in Faroe Islands. Well, Santa…

The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers as of December 2021.

The map shows the proportion of the population aged 30-34 years old, who had a tertiary education at the European level in 2019. Purple shades indicate higher proportions, and pinkish shades reflect lower proportions. It is common to show the education attainment for the age group 30-34 since it is an age group where most people have finalised their studies. The focus on this age group makes it easier to see recent trends and outcomes of policies. Overall, over 40% of Europeans aged 30-34 years old had a tertiary education in 2019. Young people in the Nordic countries are among the most educated, with approximately half of 30 to 34-year-olds achieving a tertiary education across all Nordic countries. The highest proportions can be found in the capital regions. Stockholm is particularly noteworthy, with over 60% of 30 to 34-year-olds having had a tertiary education. Regions with prominent universities also stand out – for example, Skåne, Uppsala, Västerbotten and Västra Götaland (Sweden), Trøndelag (Norway) and Østjylland (Denmark).

This map shows the ratio between household disposable income (HDI) and gross regional product (GRP) at regional level across Europe in 2016. A ratio of around 1 indicates that HDI and GRP are at a similar level, in which case GRP could be an accurate measure of individual material wellbeing. A lower value indicates a greater difference between the two values, meaning that GRP cannot be used as a proxy for highlighting the economic situation of the population in a region. The ratio between HDI and GRP for European regions in 2016 ranges from 0.24 to 0.92, with an average of 0.60. Regions with the lowest ratio are mostly capital city regions, although regions of Ireland also have a low ratio. This is partly due to the presence of large companies transferring some of the generated income to foreign countries (e.g. the country where the company’s headquarter is located). Regions, where HDI is similar to GRP, are mostly peripheral regions in countries such as France, Hungary, Poland and the United Kingdom, for instance in Southern Scotland (0.91). In the Nordic regions (NUTS 2 level), the value of HDI is on average half the value of GRP, meaning that GRP cannot be used as a good proxy for highlighting the economic situation of the population in these regions. Many regions in Norway and Sweden, as well as Iceland, are close to this Nordic average value of 0.52, which is lower than the European average. Read the digital publication here.

This map shows the regional innovation scoreboard (RIS) in the European regions in 2019. The small map shows the innovation scoreboard at national level. The index shows the performance of innovation systems, classified into four main performance groups (leader, strong, moderate and modest). The European innovation scoreboard provides a comparative assessment of the research and innovation performance in European countries. It assesses the relative strengths and weaknesses of national innovation systems and helps countries identify areas they need to address. The Regional innovation scoreboard (RIS), a regional extension of the European innovation scoreboard, assesses the innovation performance of European regions on a limited number of indicators. The RIS 2019 covers 238 regions across 23 EU countries, as well as Norway, Serbia and Switzerland. Cyprus, Estonia, Latvia, Luxembourg and Malta are also included at country level. The RIS 2019 is a comparative assessment of regional innovation based on the European innovation scoreboard methodology, using 18 of the latter’s 27 indicators. It provides a more detailed breakdown of the performance groups with contextual data that can be used to analyse and compare structural economic, business and socio-demographic differences between regions. The Nordic regions are doing well in an overall RIS comparison regarding innovation performance. There are, however, considerable differences in innovation performance between the Nordic regions. For example, the capital regions have higher levels of innovation performance than more rural and peripheral regions, according to RIS 2019. This is often due to the critical mass of companies and the spatial significance of the proximity of firms and entrepreneurs, enabling knowledge-sharing and spill-over effects. Read the digital publication here.