OECD House Price Index. Change 2020Q2–2021Q2
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.
Change in new registered cars 2019-2020
The map shows the change in new registered passenger cars from 2019 to 2020. In most countries, the number of car registrations fell in 2020 compared to 2019. On a global scale, it is estimated that sales of motor vehicles fell by 14%. In the EU, passenger car registrations during the first three quarters of 2020 dropped by 28.8%. The recovery of consumption during Q4 2020 brought the total contraction for the year down to 23.7%, or 3 million fewer cars sold than in 2019. In the Nordic countries, consumer behaviour was consistent overall with the EU and the rest of the world. However, Iceland, Sweden, Finland, Åland, and Denmark recorded falls of 22%–11% – a far more severe decline than Norway, where the market only fell by 2.0%. The Faroe Islands was the only Nordic country to record more car registrations, up 15.8% in 2020 compared to 2019. In Finland, Iceland, Norway, and Sweden, there were differences in car registrations in different parts of the country. In Sweden and Finland, the position was more or less the same in the whole of the country, with only a few municipalities sticking out. In Finland and Sweden, net increases in car registrations were concentrated in rural areas, while in major urban areas, such as Uusimaa-Nyland in Finland and Västra Götaland and Stockholm in Sweden, car sales fell between 10%–22%. Net increases in Norway were recorded in many municipalities throughout the whole country in 2020 compared to 2019.
Bankruptcies in 2020 by industry and region
The map shows the most affected industry by relative increase in concluded business bankruptcies 2020 compared to 2015–2019 average. Regional patterns in business failures are linked to factors ranging from the effectiveness of the measures adopted by the various governments to the exposure of regional economies to vulnerable sectors. Regions with higher numbers of bankruptcies tend to reflect the concentration of economic activity in sectors particularly affected by the pandemic. It comes as little surprise that Accommodation and food service activities were the industries with the largest increase in business bankruptcies in 2020 compared to the 2015–2019 baseline. In the Nordic Region as a whole, the number rose by 28.6%. This pattern is also discernible at the regional level. Hotels and restaurants were the activities with the biggest increase in the number of bankruptcies in a significant number of Swedish, Norwegian and Finnish regions. Other sectors suffering higher-than-average numbers of business bankruptcies are service industries, particularly those requiring closer social interaction, like Education (16.5% increase), Other service activities (12.0% increase) and Administrative and support service activities (7.9% increase). The logistics sector was also greatly affected, with major impact localised around logistics centres and transport nodes in the different countries. In the capital regions of Oslo, Stockholm and Helsinki, Transportation and storage was the sector with the largest increase in bankruptcies. Wholesale and retail trade; repair of motor vehicles and motorcycles was the industry to suffer the most in Denmark and several Finnish and Swedish regions.
Relative change in the number of business bankruptcies
The map shows the relative change in the number of concluded business bankruptcies by region, 2015–2019 average compared to 2020. At sub-national levels, the distribution of business bankruptcies does not show a clear territorial pattern. In Iceland and Denmark, businesses in the most urbanised areas, including the capital regions, seem to have been those that benefited most from the economic mitigation measures (-23.9% in Höfuðborgarsvæðið and -24.4% in Region Hovedstaden). By contrast, Oslo is the only Norwegian region where there were more business bankruptcies in 2020 compared to the 2015–2019 baseline (1.9% increase). Most Norwegian regions did, in fact, have fewer bankruptcies in 2020, particularly in the western regions. One plausible explanation for this could be that the number of business failures during the baseline period was especially high in western Norway due to the fall in oil prices in 2014–2015. In Sweden the situation is even more mixed. Here, businesses in urban areas seem to have been more exposed to the distress caused by the Covid-19 pandemic. The most urbanised regions in the Stockholm-Gothenburg-Malmö corridor registered a greater increase in liquidations (Jönköping, Kronoberg and Södermanland regions saw surges of around 20%). However, predominantly rural regions in Sweden, such as Västerbotten and Jämtland, also recorded higher numbers of bankruptcies than average (9.8% and 8.8% increase, respectively). In Finland, the impact was greater in Lapland (26.9%) and around Helsinki (Uusimaa, 25.9%) than in the central parts of the country. Åland also experienced a moderate rise in business bankruptcies in 2020 (4.0%), mostly related to the tourism sector.
Contraction of national economies in Western Europe, 2020
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.
Price development for Danish single-family homes
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.
Gross Value Added (GVA) change 2019-2020
The map shows the change in regional Gross Value Added (GVA) from 2019 to 2020 (in fixed prices). As shown in the map, aggregated production levels, in terms of Gross Value Added (GVA), contracted in nearly all of the Nordic regions between 2019 and 2020. In general, the variability was comparatively smaller within each country than it was between countries, even when comparing regions with similar economic profiles from different countries. On average, the impact was greater on regions in Sweden and Finland than those in Denmark. Still, some relevant territorial patterns emerge from the changes to regional GVA shown in the map. The contraction was larger in regions with higher dependence on tourism services and hospitality (Åland and some municipalities in South Karelia, Finland, and Bornholm, Denmark), as well as on mass-market retail and logistics, particularly in the areas surrounding the capital regions (Södermanland and Västmanland in Sweden and Greater Copenhagen in Denmark). In Sweden and Finland, a remarkable regional divide can also be traced between territories specialised in transformation sectors with limited vulnerability to the impact of Covid-19, including forestry and specific types of processing (e.g. pulp, cement), like Nord Ostrobothnia, Kainuu and Pirkanmaa in Finland, and Gotland, Västerbotten and Örebro in Sweden. Aggregated output in these regions fell less than in regions with greater exposure to industrial manufacturing, like Kymenlaakso in Finland and Kronobergs in Sweden. Similarly, the impact on the financial centres in Denmark (Greater Copenhagen) and Sweden (Stockholm) was less than regions with mid-sized cities and diversified urban economies, like Vestjylland (Århus) in Denmark and Upsala in Sweden. Interestingly, the shock to the Finnish economy was greater in the Helsinki metropolitan area (-3.6% Uusimaa) than it was for the Tampere region (-0.5% in Pirkanmaa). This may be due to the relatively higher concentration…
Nordic cross-border co-operation committees 2021
The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers as of December 2021.
Cross-border commuters as a share of total employees in the Nordic Region 2015
The map shows the share of cross border commuters in the total employees with residence in a NUTS2 Nordic Region in 2015. The darker the blue, the higher the share. For the most NUTS2 regions in the Nordic, the percent is lower than 0,5%, indicating the commuting workers are the absolute minority in the total employed people. Åland (2,6%) and the South Sweden region (2,7%) stand out with more than 2% of employees in the region commuting cross-border for work. The destination country for Åland workers is Sweden, while for Swedish workers living in the south is Denmark. The commuting pattern is also apparent for the Swedish NUTS2 regions along the border line with Norway, with relatively higher percent of cross border workers commuting to Norway compared with other Nordic NUTS2 regions. At a finer scale (e.g., NUTS3) would show higher percentages in a number of regions, e.g., by taking only the NUTS 3 region – Skåne instead of the NUTS 2 region South Sweden (Skåne+Blekinge) or the border regions between NO and SE.
Cross-border commuters to other Nordic countries for work 2015
The map builds on statistics of cross-border commuters with residence in a NUTS2 Nordic region commute for work in 2015. For each NUTS2 region, the map shows the total number of commuters who commute to other Nordic countries for work. The number of commuters is categorised into three groups visualised in different shades: the darker, the higher the number of commuters. In addition, the most common country these commuters commute to from each region is identified by specific colours. For example, the darkest red indicates a region with at least 2,000 commuters working in another Nordic country, of which the largest group number of commuters works in Denmark. The most commuters were from the region of South Sweden (16 543) in 2015, and the majority of them commuted to Denmark for work. Norway is the most popular destination for work commuters in the Nordic Region, e.g., all Swedish regions except for the South Sweden region, all the regions in Denmark except for the Copenhagen region, and Iceland. Sweden is more attractive for work commuters living in Finland, Copenhagen region, and bordering regions in Norway.
Nordic cross-border co-operation committees 2020
The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers.
Tourism gross value added as a share of GVA 2018
Tourism gross added value (GVA) corresponds to the part of GVA generated by all industries in contact with visitors. This indicator is measured as a percentage of total GVA at basic prices in 2018 (No data for Greenland and Faroe Islands; data for Finland includes Åland). Data were retrieved from each country’s tourism satellite account. Åland and Iceland stand out as the country or territory where tourism added value accounts for over 10% of the total GVA. For Åland, tourism is so important an industry that added value related to tourism is equivalent to nearly 20% of the total GVA in Åland. The share of tourism related GVA is close to 4% of the total GVA in Norway and Denmark, and lower than 3% in Finland and Sweden.
Accessibility gains from virtual health rooms in Västerbotten
To secure better access to general practitioners for the rural population, the region of Västerbotten has developed the concept of virtual healthrooms (VHRs). These VHRs are unstaffed, which means that they have no regular health personnel in situ. They are equipped with distance-spanningtechnology, which means that patients can go there to take consultations from a practitioner online, conducting health checks such as measuringblood pressure or heart rate. The coloured patches on the map show those populated areas in Västerbotten where inhabitants can expect a reduction of travel distance to primary health care through the implementation of VHRs. The coloured patches are populated areas in Västerbotten (by 1000*1000m grid) with improved accessibility of health care resulting from the implementation of virtual health rooms. The colour indicates the total distance reduced.Distance is measured as being via the road network. The average distance to the closest primary health care facility (health centre or virtual health room) is 6 km for the overall population in Västerbotten. The implementation of VHRs means that around 3.5% of the 270,000 inhabitants of Västerbotten experience increased accessibility toa primary health care service. The travel distance for this portion of the population has been cut by almost 50%, from 42 km per person to 23 km per person. Patients may also use virtual health rooms to conduct teleconsultations with health professionals at specialised hospitals, which creates even greater potential from an accessibility standpoint.
Accessibility of highly specialised care in Västerbotten
The map illustrates the accessibility of highly specialised care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The only hospital offering highly specialised in-patient care is located in Umeå, and half of the regional population (51.0%) can reach the hospital within a 40-minute car ride. For the inhabitants of Storuman, however it takes more than a two-hours car ride to access specialised care in the hospital. The accessibility of health care services for rural dwellers are improved by means of distance-spanning digital solutions. Instead of making the effort of physically visiting a health care facility, they can access video consultations with general practitioners via virtual health care rooms, and then decide whether a follow-up physical visit to the specialised hospital is needed.
Accessibility of in-patient care in Västerbotten
The map illustrates the accessibility of in-patient care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The accessibility of in-patient care shares similar characteristics to that of access to dropin out-patient care. The ten facilities mentioned above provide in-patient care, too. The only difference is that two facilities outside the region were included in the analysis. But they did not alter the overall picture, suggesting that inhabitants living along the border line tend not to cross the border to seek in-patient care. These hospitals are accessible to 56.3% of the population within a 10-minute car ride, and 97.1% of the population within a one-hour car drive.
Accessibility of out-patient drop-in care in Västerbotten
The map illustrates the accessibility of out-patient drop-in care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. Health care facilities offering out-patient drop-in care are characterised by even geographical distribution across Västerbotten. Each inland municipality has one such heath care facility, apart from Norsjö. All ten facilities offer drop-in care during both office hours and evenings and weekends (24/7). According to the results of our accessibility analysis, 56.% of the regional population can access drop-in out-patient care within a ten minute car ride. Within a half-hour car ride, the drop-in care service is available to 83.3% of inhabitants.
Accessibility of primary care in Västerbotten
The map illustrates the accessibility of primary care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The 37 health care facilities in the region contribute to widespread coverage of primary outpatient care for the population in Västerbotten. Over 80% of the inhabitants can access such health care services within a 10-minute car ride, and a half-hour car ride can cover 95.8% of the regional population. In general, coastal municipalities have easier access to health care services than inland municipalities. As one of the inland municipalities, Storuman accommodates one health care facility which provides most of the health care services expected in relation to highlyspecialised care.
Labour market impacts of COVID-19
On May 17, 2020, 94% of the world’s workers were living in countries with some form of workplace closure measures in place (ILO, 2020). While it is too early to make predictions about the long-term consequences of this, it is possible to make some observations about the short-term labour market impacts in the Nordic Region. The map shows the number of people who registered as unemployed in April 2020 compared with the number of people who registered as unemployed in April 2019 at the municipal level for Denmark, Finland, Norway and Sweden and Åland Islands and at the national/territory level for Iceland and the Faroe Islands. The shading represents the increase in percent, with darker colours showing higher relative increases compared to the previous year and lighter colours lower relative increases. Municipalities shaded in blue on the map did not experience an increase in unemployment registrations in April 2020 compared to April 2019. Overall, the number of unemployment registrations across the Region was 38.9% higher in April 2020 than in April 2019. This increase equates to a total of 220 354 Nordic workers and has affected almost all Nordic municipalities and regions to some degree. Proportionally speaking, Norway saw the largest increase (69%), followed by Iceland (59%), Denmark (48%), Sweden (41%), and Finland (24%). Though between-municipality variation is evident, the greatest differences appears to be between countries. Interestingly, many Swedish municipalities along the southern coast between Sweden and Norway saw increases more consistent with the overall trend observed in Norway. This may be a reflection of the prevalence of cross-border commuting in these regions. It is important to note that the labour market situation in April 2019 has some baring on the results shown on the map. For example, the appearance of a sharper relative increase in Norway is primarily…
At-risk-of-poverty rate 2011-2018 change
The map shows the “at-risk-of-poverty” (AROP) rate in the Nordic Region. For the period from 2004 to 2018, the AROP rate increased in all Nordic countries except Iceland. This trend was strongest in Sweden. In Finland the AROP rate has been decreasing during the past few years, in line with what has previously been indicated – namely, on account of economic turmoil. This points to one of the weaknesses of using the AROP rate alongside several other measures of inequality. That is, while people have become poorer due to the economic crisis, the at-risk-of-poverty rate has paradoxically gone down. In addition, the AROP rate for Finland is higher in 2018 than it was in 2004. Looking at these trends on a regional level over a period of time (between 2011 and 2018), we can see that the AROP rate has decreased in almost all areas of Finland, whereas the pattern is rath er more varied in the other Nordic countries (we can also see a cohesive area in the south of Denmark where the AROP rate has decreased.) Again, Sweden has the most regions displaying increases in the AROP rate. Finland and Sweden contain the largest differences between the regions with the highest and lowest AROP rate. Hence the greatest regional differences are to be found in Sweden and Finland. Sweden also has the highest average AROP rate. About the At-risk-of-poverty The at-risk-of-poverty rate is a common measure of relative poverty and social inclusion. Most notably, it has been used for monitoring the EU2020 goal of inclusive growth. The at-risk-of-poverty rate is normally defined as “the share of people with an equivalised disposable income (after social transfer) below the at-risk-of-poverty threshold, which is set at 60% of the national median equivalised disposable income after social transfer.” (Eurostat). The indicator is…
Income and inequality typology 2017
The map shows a typology, combining two indicators to display income disparities between and within municipalities. The map combines measurements of household disposable income (HDI) and the Gini Index to create four “types” of income distribution. Household disposable income is a common measure of income inequality. It measures the capacity of households (or individuals) to provide themselves with consumable goods or services. Comparing average HDIs is a convenient way of understanding inequality between municipalities. The Gini Index measures the extent to which the distribution of household income deviates from an equal distribution level. The Gini Index is therefore useful in understanding the inequality that exists within municipalities. Combining these measurements provides a comprehensive geographic overview of income in equality across the Nordic Region, both within and between municipalities. The municipalities shaded in yellow on the map have an average HDI above the Nordic average, as well as a Gini coefficient above the Nordic average (i.e. high income, but unevenly distributed). This category includes most of the wealthiest municipalities, including municipalities in the capital regions – e.g. most municipalities in the Stockholm Region (Lidingö, Danderyd, Ekerö, Täby, Sollentuna), Copenhagen (Gentofte, Hørsholm, etc.), and Helsinki (Kauniainen). Several municipalities in southern Sweden and Denmark also fall into this category. Most of these have average HDIs just above the Nordic average. The second category (blue on the map) consists of municipalities with HDI above the Nordic average and a Gini coefficient below the Nordic average (i.e. high income and even distribution). Most municipalities in this category are in Norway. Norway has a higher HDI and more even distribution than the other Nordic countries. The third category (green on the map) consists of municipalities with both an HDI and a Gini coefficient below the Nordic average (i.e. lower income, but more evenly distributed). This category…