The disparity map shows that Finland can be differentiated into four spatial types with distinct socioeconomic advantages and disadvantages. The legend uses associative colours on purpose: shades of green show areas that currently fare better in the overall assessment, and seem to be better prepared for the challenges of the future – at least for the majority of people. The ocre colour shows areas that frequently have indicator values close to the national average. The violet colour is used to map out areas with a majority of negative indicator values – areas in need of dedicated policy attention. Based on this colour interpretation the map shows basically three settings for disparities: average, above average, and below average.
1. Above average: The generally better off prospering urbanised areas with drivers of social exclusion and the urban catchments (light and darker green in figure 1) frequently form adjacent delineations with an urban core as the nucleus for growth and prosperity. The spatial extent can be understood as the area of reach for functional relationships between core and suburban or exurban commutersheds on the one hand, and other spill-over effects from the urban core. Economies of scale play a decisive role in the explanation of the economic attractivity of these areas: High quality infrastructure and services, human ressources and the proximity to regional and supra-regional markets motivate economic advantages that frequently outweigh higher locational costs for business entrepreneurs and enterprises. Continued growth, however, can lead to new risks of social exclusion if economic benefits are unequally distributed and remain intangible for parts of the population. This is frequently the case when increasing demand conflicts with limited ressources. Market forces are then likely to start a vicious cycle of rising living costs, pressure on housing, and in due course to segregation and/or displacement of disadvantaged households with follow-on effects for example on traffic and aspects of social cohesion. These two spatial types are inhabited by the majority of the Finnish population: 3.95 million people (71.6% of the population) live in the 76 municipalities (25.9% of the all Finnish municipalities) of these two clusters.
2. Average: Finland’s solid middle (ocre colour in figure 1) gives home to 0.95 million people (17.2% of the total Finnish population of 5.5 million inhabitants) and 109 municipalities (37.% of a total of 293 municipalities). It spans in a wide circle from southern inland regions across western parts of the country to sparsely populated but large municipalities in the north. This spatial type is characterised by a majority of indicator values close to the national average (see table 2).
3. Below average: The backcountry of Finland (violet in figure 1) contrasts the developments in the urbanised areas and their catchments. This is where young people leave the countryside in large numbers, for educational purposes, searching for job opportunities and/or urban lifestyles. The population is therefore older on average. Some areas struggle with vacancies on the housing market and oversized infrastructure due to a shrinking population base. The remaining workforce needs to support more elderly people and children. The share of employees in the health sector is comparatively high. Many people work in industry, mining and agriculture. The perspectives in the backcountry can potentially become problematic if old industries, e.g. in mining, run out of ressources and phase out, and/or (digital) automation processes lead to a replacement of labourers and continued shrinkage. In total, the sparsely populated Finnish backcountry is populated by 0.5 million people (10.5% of the Finnish population) in 109 municipalities (37.2% of all Finnish municipalities).
There are distinct differences between urban and rural regions but also between inland and coastline regions. Southwest Finland has the highest employment rates (between 76.2% and 85%). Furthermore, a concentration of high values can be found in the coastline regions around the cities of Helsinki, Vaasa and Turku which is due to the diversity of job opportunities. The inland is characterised by centres with high employment rates (e. g. regions around Tampere, Kuopio or Joensuu) due to universities and tertiary educational opportunities. The lowest employment rates are can be found in rural areas and the regions bordering Russia. One exception here is northern Finland (Lapland) which has higher employment rates due to the service and tourism sector.
In general, the employment rate increased throughout Finland between 2013 and 2018. A slight decrease can be found only in two municipalities: in Puolanka (-0.9%) in central Finland, which has a rather low employment rate (57.2% in 2018) and in Pukkila (-1.2%; employment rate in 2018: 75.8%), located in the south of Finland. Northern Finland, especially Lapland, is characterised by a sharp increase and a high employment rate due to the service and tourism sector (e. g. Kittilä: +7.5%). Besides, scattered southern inland regions recorded a high increase. A lower increase can be found in southwestern Finland and around the coastline regions (characterised by a high employment rate) and in the southern regions bordering Russia (characterised by a lower employment rate).
The demographic dependency ratio shows higher dependencies in rural regions: rural inland areas and peripheral regions display a very high ratio which is mainly due to high shares of older people (80–100%). Especially in some regions in the triangle between Tampere, Lahti and Mikkeli ratios over 100% can be found. In contrast, dynamic and prospering cities have the lowest rates (e.g. Helsinki/Espoo, Turku, Oulu) due to job attractiveness for working people.
The distribution of the turnover of establishments of enterprises shows a mixed geographic pattern with no clear geographic disparities: a clear concentration of high turnovers can be found on the southern coastline, e.g. Espoo (550) and Vantaa (370). Other locations with high turnovers are distributed over the country (e.g. Tornio (929) and Sodankylä (301) in the North, Sotkamo (291) and Joensuu (249) in the East, Pyhäjärvi (251) and Jämsä (272) in central Finland). In general, it can be said that the geographic distribution of turnovers per person correlate with the population density and that high values correlate with overall economic productivity. But an explanation of the geographic distribution requires a deeper look into the different economic sectors (e. g. forest industries or tourism).
The at risk of poverty rate for children is related to a lower income level. The highest at risk of poverty rates for children (20% or above) can be found in rural regions outside the urban centres while the lowest rates (under 7%) are located in the municipalities around the major cities of Turku, Helsinki and Vaasa. Finland is a welfare state; “everyone has the constitutional right to public health care, social care and education. All children receive a child allowance and all retired people are entitled to minimum old-age pensions.”1
The regional distribution of people with a university degree shows distinctive disparities between larger cities and rural areas. As expected, the university cities and nearby regions have the highest ratio (e.g. Espoo (38%), Helsinki (36%), Tampere (29%)). Besides, some municipalities without universities stand out, e.g. Ylivieska (19%)2 and Kokkola (18%)3. A reason can be a higher demand of local industries for highly qualified workers (e.g. mechanical engineering).
High indicator values show that young people's access to the labour market and the associated future opportunities are more difficult in a region. Therefore, education is the key to overcoming poverty. Due to the incomplete data situation, interpretation is rather difficult. Basically, there is no clear geographic pattern: disparities between urban and rural regions cannot be found (unlike the concentration of poverty). However, there are striking differences between individual regions: the highest school dropout rate can be found in Simo (13.3%), north of Oulu whereas the lowest rate can be found in Vaala (0%), southeast of Oulu.
The distribution of annual gross income reveals disparities between rural and urban regions. Kauniainen, near Helsinki, has by far the highest income (€139,000) and share of university-educated people (48%). It is a small town with the lowest local tax rate and can therefore be treated as a statistical outlier1. Besides, the region around Helsinki has the highest income (€70,000–€80,000), followed by Turku, Vaasa, Tampere and the region around Oulu (€60,000–€70,000). In contrast, the lowest income can be found in rural inland areas and the peripheral regions in the east (€39,000–€45,000).
The distribution of the development of annual gross income reveals disparities between rural and urban regions. A high increase can be found in and around urban regions which are characterised by a high income level like Helsinki (+€4,805), Turku, Tampere, Jyväskylä and Oulu but also in Lapland (e. g. Muonio +€5,460). By contrast, the regions in and around Vaasa (+€451) which are also characterised by a high income level have much lower increase or even a decrease (e. g. Maalahti, south of Vaasa: -€421). Further regions with a lower income increase or decrease are located in rural inland regions and the peripheral areas in the east.
The distribution of the number of employees in the social welfare and health care sector shows disparities between rural and urban regions. Many inland and rural municipalities have a higher share of employees in the health sector (e.g. Pieksämäki (1,118), Kemijärvi (725)) due to a higher average age of the population with a higher demand of social and health care services. Besides, larger cities like Helsikini, Turku and Tampere have a high number of employees in this sector which is probably related to more specialised health services. This indicates a geographic hierarchy of healthcare services.
High home loans have diverged regionally over the past decade, particularly between the Helsinki metropolitan area, other urban centres and the rest of the country1. The highest values can be found in dynamic centres where housing is more expensive and subject to greater demand pressure (e.g. Espoo (155,000), Helsinki (145,700)) – these disparities are a “consequence of urbanisation“2. The majority of households (approximately 64%) are home owners which leads to a socially unjust lack of affordable housing in growth centres and therefore to a bottleneck for employment and economic growth.
The distribution of disposable cash income of household-dwelling units reveals distinct disparities between rural and urban regions and between cities. The highest values (e.g. Liminka, south of Oulu: €48,919) can be found in the surrounding municipalities around larger cities like Helsinki (€34,145), Oulu (€31,096), Vaasa (€30,525), Tampere (€28,140) and Turku (€27,299) while the values in the cities are (far) below the average. Besides, low values can be found in rural inland areas and peripheral regions in the east (lowest in Rautavaara, eastern inland: €25,391).
The distribution of the change of disposable cash income shows no clear spatial pattern. A high increase can be found in Lapland (e. g. Inari: €2,190), the region of Oulu (e. g. Liminka: €1,773) and in parts of western Finland and in Helsinki (€1,735). A lower increase as well as decreases can be found for example in parts of the southeastern border regions, in regions around Helsinki and around Vaasa.
The geographic pattern of rental prices is quite similar to that of home loans: the Helsinki metropolitan area and other urban centres subject to greater demand pressure are characterised by high rents (e.g. Vaasa, Tampere, Turku, Oulu) and stand out from the rest of the country. This development is a “consequence of urbanisation“ and comes along with social injustice which is a bottleneck for employment and economic growth.
High loan stocks can be an indication of structural underfunding of a municipality or city, for example due to high social costs. The distribution shows no clear geographic pattern. However, there is a noticeable concentration of high values along the west coast between Oulu and Vaasa (e.g. Reisjärvi: €11,005). Besides, high values can be found in scattered municipalities in southern Finland (e.g. Lahti: €6,645) while lower values can be found in the eastern inland and on the southwestern coast between Turku and Tampere.
Typically, voter turnout correlates with education and wealth and so the figure shows regional disparities between the southwestern coastal regions and the eastern inland regions: highest turnout can be found in the region around Vaasa (over 80%), which is the cultural centre of Swedish Finland, followed by urban regions like Helsinki, Tampere and Turku (75-80%). Apart from these major centres, the voter turnout is below 67%, especially in municipalities on the Russian border and in rural inland areas.
In Finland, broadband provision as a percentage of households spans between 1 up to 100% while low values show spatial deficiencies in (digital) infrastructure provision, opportunities for participation and (digital) business opportunities. The highest rates (over 90%) are found on the one hand in the urban centres (e.g. Helsinki and region around Oulu) and on the other hand in many suburban municipalities. In contrast, many rural municipalities have a lower share of households with broadband connection. But in general, there is no clear spatial concentration and the values correlate to some degree with the population density.
The migration pattern shows distinct disparities between urban and rural regions. Rural areas are mainly confronted with a continuous loss of inhabitants due to internal migration. Among the cities with the highest immigration are Tampere (+51), Turku (+36), Helsinki (+35) and Vantaa (+31), followed by other cities with strong pull factors like Kuopio (+27), Joenssu (+19) and Oulu (+15). In contrast, 244 out of the 295 regions in Finland have a negative net migration (83%), among them Kristinestad with the highest emigration per 1,000 inhabitants (-150).