EUROPEAN COMMISSION

Brussels, 17.6.2020

SWD(2020) 109 final

COMMISSION STAFF WORKING DOCUMENT

ADDITIONAL FIGURES, MAPS AND TABLES ON THE KEY ASPECTS OF DEMOGRAPHIC CHANGE AND ITS IMPACT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

on the impact of demographic change

{COM(2020) 241 final}

Contents

I: The drivers of demographic change in Europe

1. Increasing life Expectancy

1.1Life expectancy in the EU by sex

1.2Life expectancy in Member States

1.3Healthy life years7

2. births and fertility rates

3. Ageing population

3.1Median age

3.2Population age structure

3.3Old-age dependency ratio

4. Smaller households

4.1 Trends of households by type

4.2Trends of households with and without children

4.3Households in 2019

4.4Households by number of children in 2019

4.5Change of the number of persons in household from 2010 until 2019

4.6Differences in household composition by age and sex

5. Mobile Europe

5.1Immigrants and emigrants

5.2Age structure by citizenship

6. changing population

6.1Natural population change in the EU

6.2Demographic balance in the Member States in 2018

II: THE IMPACT OF DEMOGRAPHIC CHANGE – territorial differences

7. regional and local dimension

7.1GDP (in PPS) per person employed

7.2 Natural population change and net migration

8. INFRASTRUCTURE AND ACCESS TO SERVICES    40

8.1    Accessibility to passenger flights    40

9. URBAN, INTERMEDIATE AND RURAL REGIONS    42

9.1    Population by level of regional GDP per head    44

9.2    Population in low-income regions by urban-rural typology    44

9.3    Population in rapidly shrinking regions by urban-rural typology    45

9.4    Population in rapidly shrinking, low-income regions by urban-rural typology    45

9.5    Crude birth rate by urban-rural regional typology    46

9.6    Crude death rate by urban-rural regional typology    47

9.7    Total fertility rate by urban-rural regional typology    48

10. CITIES, TOWNS AND SUBURBS, AND RURAL AREAS    52

10.1    Employment rate by degree of urbanisation    52

10.2    Unemployment rate by degree of urbanisation    53

10.3    NEET by degree of urbanisation    54

10.4    Early leavers from education and training by degree of urbanisation    55

10.5    At-risk-of-poverty rate by degree of urbanisation    56

10.6    Median equivalised income by degree of urbanisation    57

10.7    Tertiary educational attainment by degree of urbanisation    58

10.8    People who perceive their health as good health by degree of urbanisation    59

10.9    Proportion of population born outside EU    60

10.10 Proportion of population born in another EU Member State by degree of urbanisation    61

This Commission Staff Working Document complements the report of the Commission on the impact of demographic change (COM(2020) 241 final) with additional figures, maps and tables. 1 The document follows the sequence and order of topics from the report.

I: The drivers of demographic change in Europe 2

1. Increasing life Expectancy

The most commonly used indicator for analysing mortality trends is life expectancy at birth: the mean number of years that a person can expect to live at birth if subjected to current mortality conditions throughout the rest of their life. It is a simple but powerful way of illustrating the developments in mortality.

1.1Life expectancy in the EU by sex

Life expectancy at birth in the EU-27 was estimated at 81.0 years in 2018, reaching 83.7 years for women and 78.2 years for men.

Overall, between 2002 (the first year for which life expectancy data became available for all EU Member States 3 ) and 2018, life expectancy in the EU-27 increased by 3.4 years, from 77.6 to 81.0 years; the increase was by 2.8 years for women and 3.9 years for men. Population projections 4 built on this ascending trend, with the assumptions of increasing life expectancy at birth in the EU Member States, and gradual convergence among the EU Member States. Under these assumptions, the average life expectancy at birth in the EU would increase by 2070 to 86.1 years for men, and 90.3 years for women.

Figure 1. Life expectancy at birth for men, 2002-2070

Observed (2002-2018) and projected (2019-2070) data (years)

Source: Eurostat (online data tables: demo_mlexpec and proj_19nalexpy0)

Figure 2. Life expectancy at birth for women, 2002-2070

Observed (2002-2018) and projected (2019-2070) data (years)

Source: Eurostat (online data tables: demo_mlexpec and proj_19nalexpy0)

1.2Life expectancy in Member States

In the years between 2000 and 2018, the rise in life expectancy at birth for men in the EU Member States 5 ranged from a minimum of 3.1 years (in Bulgaria) to a maximum of 8.4 years (in Estonia). For women, the increase ranged from 2.1 years (in Germany) to 6.3 years (in Estonia).

There are still differences between countries. In 2018, the differences between the highest and lowest life expectancies among EU Member States amounted to 11.1 years for men and 7.7 years for women.

In 2018, the life expectancy for women is higher than the life expectancy for men. With a gender gap of 5.5 years of life in 2018, newly born women in the EU-27 should generally expect to outlive men. The projections suggest a further reduction of this gap, down to 4.5 years by 2050 and to just over 4 years by 2070. Furthermore, this gap varies between EU Member States.

Table 1. Life expectancy at birth, 1980-2018

(years)

Source: Eurostat (online data table: demo_mlexpec)

1.3Healthy life years

In 2018, the number of healthy life years at birth 6 was estimated 64.2 years for women and 63.7 years for men. The difference between women and men is lower for healthy life years than for life expectancy.

The time series show a changing trend over the period 2010-2018: slight decrease in the first years, followed by an increase from 2015 onwards (most notably for men). Higher values in 2015 compared to the past years are due to a major methodological change introduced by Germany, which led to the break in series also at the EU level. The new time series, initiated in 2015 shows that the number of healthy life years is increasing slightly at EU level.

Figure 3. Healthy life years at birth, EU-27, 2010-2018

Number of years that a person is expected to continue to live in a healthy condition

 
Note: break in series in 2015

Source: Eurostat (online data tables: hlth_hlye)

Table 2. Healthy life years in absolute value at birth – women (2010-2018)

Note: (b) break in the series, (p) provisional

Source: Eurostat (online data tables: hlth_hlye)

Table 3. Healthy life years in absolute value at birth – men (2010-2018)

Note: (b) break in the series, (p) provisional

Source: Eurostat (online data tables: hlth_hlye)

2. births and fertility rates

The most widely used indicator for analysing population due to birth is the total fertility rate: this is the mean number of children that would be born alive to a woman during her lifetime if she were to pass through her childbearing years conforming to the age-specific fertility rates of a given year. A total fertility rate of around 2.1 live births per woman is considered the replacement level in developed countries: in other words, the average number of live births per woman required to keep the population size constant in the absence of migration.

Fertility declined steadily from the mid-1960s to the turn of the century in the EU countries. In most of the EU Member States, the total fertility rate diminished considerably between 1980 and the end of 1990s. After reaching a low point around year 2000, the total fertility rate increased in most Member States. Since 2011, fertility stays roughly stable at EU-27 level.

In the past 45 years, in general, total fertility rates among the EU Member States have been converging. In 1970, the disparity between the highest rates for which data exist and the lowest rates ) was around 2.0 live births per woman. By 1990, this difference had decreased to 1.1 live births per woman. By 2010, the difference had fallen to 0.8 live births per woman and by 2018 it had narrowed to 0.6. According to the projections baseline scenario, the figure would be down to 0.37 in 2070.

Table 4. Total fertility rate (1960-2018)

(live births per woman)

Source: Eurostat (online data table: demo_find)

Figure 4. Total fertility rate, 1960-2070

Observed (1960-2018) and projected (2019-2070) data (live births per woman)

Note: For the interval 1960-2000, the values are indicative and should be understood as minimum or maximum values among the countries for which data is available.

Source: Eurostat (online data tables: demo_find and proj_19naasfr)

Figure 5. Total fertility rate, 2018

(live births per woman)

Source: Eurostat (online data table: demo_frate)    

3. Ageing population

3.1Median age 

The median age of the EU-27 population has been continuously increasing, to reach 43.7 years in 2019, up from 38.4 years in 2001. There are significant differences among Members States: 9.0 years difference between the lowest median age in Ireland and Cyprus (both at 37.7 years) and the highest median age in Italy (46.7).

The median age in the EU-27 increased by 5.3 years (on average, by 0.3 years per annum) between 2001 and 2019. It increased in all of the EU Member States, rising by more than 7 years in Romania, Lithuania and Portugal. The median age reflects the (cumulated) demographic development in each country.

Figure 6. Median age of population, 2001 and 2019

(years)

Source: Eurostat (online data table: demo_pjanind)

Map 1. Median age of population by NUTS3 7 region, 2019

(years)

3.2Population age structure

Population pyramids show the distribution of the population by sex and by age. Each bar corresponds to the share of the given sex and age group in the total population. The EU-27 population pyramid, in 2019, is narrow at the bottom and is becoming more like a rhomboid.

As the EU-27 population is projected to continue to age, by 2070, the pyramid would transform towards the shape of a pillar.

Figure 7. Age pyramid, EU-27, 2019 and projected for 2070

(%)

Source: Eurostat (online data tables: demo_pjan and proj_19np)

3.3Old-age dependency ratio

Old-age dependency ratios aim at demonstrating the level of support available to older persons by the demographic working age population. Values are expressed in percentage terms, in other words, per 100 persons of working age. This document features data for an old-age dependency ratio calculated as the ratio of the number of people aged 65 or older, compared to the number of people aged 20-64 years old.

For the EU-27, the above old-age dependency ratio stood at 34.1% in 2019: there were just less than three persons of working age for every person aged 65 or over. An increasing trend can be observed: the old-age dependency ratio increased by 5.7 percentage points (or by 20.1% of its former value) during the past decade (from 28.4% in 2009, which corresponded to 3.5 persons of working age for each person aged 65+).

Figure 8. Old-age dependency ratios, EU-27 and World

Observed or projected Eurostat data; estimated or projected UN data (%)

Sources: Eurostat (online data tables: demo_pjan and proj_19ndbi) for EU-27 data; and, for world data, United Nations, Department of Economic and Social Affairs, Population Division (2019)

According to Eurostat’s latest population projections, during the period from 2020 to 2070 the share of the population of working age (20-64), is expected to decline, while the older persons will likely account for an increasing share in the total population. Those aged 65 years or over are projected to account for 30.3% of the EU-27 population by 2070, compared with 20.3% in 2019. As a result, the EU-27’s ratio of the number of people aged 65 or over, compared to the number of people aged 20-64 years old is projected to reach 59.1% by 2070.

The old-age dependency ratio in 2019 ranged across the EU Member States from a low of 22.4% in Luxembourg and 24.0% in Ireland, with between four and five working age people for every person aged 65 or over, to highs of 38.6% in Italy, 38.4% in Finland and 37.6% in Greece, thus with less than three working age people for every person aged 65 or over.

Map 2. Old-age dependency ratio by region, 2019

(%)

Map 3. Projected old-age dependency ratio, 2070

(%)

According to the latest United Nations Population Prospects 2019 8 , the old-age dependency ratios are projected to increase at world level and for all continents. At world level, ratio of the number of people aged 65 or over, compared to the number of people aged 20-64 years old developed from 10.1% in 1960 to estimated 16.3% in 2020 and projected 34.3% in 2070.

Europe is experiencing an increase of the old-age-dependency ratio. From more than four persons of working age for every older person in 1980s, the old-age-dependency-ratio is projected to decrease to less than two persons of working age for every older person from mid-2040s onwards.

Figure 9. Old-age dependency ratio by continent

(%)

Source: United Nations, Department of Economic and Social Affairs, Population Division (2019)

4. Smaller households

4.1    Trends of households by type 

The overall number of households has increased by 7%, from 182 million in 2010 to 195 million in 2019.

Single-adult households have registered the most significant increase (18%), this growth is more significant for the households of single adults without children (19%) than for the ones with children (13%). 9  

The households in which couples (lawful unions or by contract) live have registered an increase of 4%. This growth is exclusively due to the higher number of couples living without children.

Moreover, households with two adults (not a couple) or more adults 10 decreased by 6%. The decrease over the 2010-2019 period is higher in the presence of children (-14%).

Figure 10. Households by type, EU-27, 2010-2019

(millions)

Source: Eurostat (online data table: lfst_hhnhtych)

The changes varied among Member States and also depended on the household composition. In Malta, Cyprus, Latvia and Estonia, the number of households consisting of adults living alone have risen the most in the period 2010-2019, respectively by 125%, 64%, and 55% for the last two. Spain and Italy has also recorded an increase of single adults with children (32% for both). On the contrary, Hungary and Romania registered the largest decreases of households of adults living alone with children (-23% and -14% respectively).

Households including couples have increased in Malta (42%), Poland (21%) and Belgium (18%) more than in the other EU Member States but the highest growths occurred for the households consisting of couples without children and have been registered in Malta (67%), Poland (32%), Slovakia (30%) and Slovenia (26%).

In the EU, from 2010 to 2019 the households including two adults (not a couple) or more adults decreased by 6%. In Estonia and Sweden, the number of those households has diminished by 42% and 56%. In contrast, these specific households have increased by 67% in Luxembourg and by 23% in the Netherlands.

4.2    Trends of households with and without children

Figure 11. Growth rate of households with and without children, 2010-2019

(%)

Note: Break in series in Bulgaria, Germany, Poland and Romania in 2010

Source: Eurostat (online data table: lfst_hhnhtych)

On the EU level, from 2010 to 2019, the number of households with children decreased slightly (-1%) and households without children increased by 11%. The number of households with children decreased in 16 Member States over the same period and increased in the others.

4.3Households in 2019

Figure 12. Type of household composition by country, 2019

(% of total households)

Source: Eurostat (online data table: lfst_hhnhtych)

In 2019, almost half EU households were couples (44%), ranging from 49% in the Netherlands, Czechia,    Poland and Finland to 35% or less in Sweden, Lithuania and Latvia (respectively 35%, 34% and 32%).

Four out of ten EU households were adults living alone (39%) with differences between countries, from 63% in Sweden, 52% in Estonia and Denmark to 25% in Croatia, 26% in Slovakia, 27% in Portugal, 28% in Poland and 29% in Spain and Cyprus.

Two adults that were not a couple or more than two adults lived in 17% of all households. They represented less than 10% in Sweden, Denmark, Finland and in the Netherlands. They were most frequent in Croatia (38%), Slovakia, Romania and Bulgaria (28% in all three).

Households with children accounted for 29% of the EU households in 2019. The highest rates were registered in Ireland (39%), Poland (36%), Romania, Cyprus and Slovakia (35% each). The lowest were in Finland (21%), Germany (22%), Sweden (23%) and Austria (25%).

4.4Households by number of children in 2019

Households with one child were the most common among households with children. In 2019, in the EU-27 almost half of the households with children only included one child (47%); Portugal, Bulgaria, Lithuania and Latvia showed the highest shares of households with children including only one child i.e. from 58% to 55%. Differently in Sweden, Ireland and the Netherlands, the households with one child constituted 40% or less of the households with children; households with 2 children or more were most frequent in these countries.

Figure 13. Households by number of children, 2019

(% of total households with children)

Source: Eurostat (online data table: lfst_hhnhtych)

4.5Change of the number of persons in household from 2010 until 2019

In most of EU Member States, the average size of household has decreased from 2010 to 2019, except in three countries in which it remained stable (Belgium, Denmark and the Netherlands). At the EU level, the average size decreased from 2.4 in 2010 to 2.3 persons in 2019. Latvia, Malta, Estonia and Sweden recorded the largest decreases in the household size, i.e. 10% or more over the whole period.

In 2019, Croatia had the highest average number of persons living per household among the EU Member States with 2.8 persons followed by Ireland, Romania, Cyprus, Poland and Slovakia with all 2.6 persons in average. At the other end, two or less persons lived in average per household in Sweden (1.8 persons) and in Finland, Estonia, Germany and Denmark, all those recording an average of 2.0 persons in 2019.

4.6Differences in household composition by age and sex 

In the EU, for persons between 15 and 64 years, approximately the same proportions of men and women live alone (19% each), in couple (48% for men, 51% for women) and with another adult (not a couple) or more adults (33% and 30% for men and women respectively).

Conversely, differences are visible among persons aged 65 and over. In the EU, four out of ten women live alone comparing to 19% of men. Four out of ten women and six out of ten men live in a couple, likely because women on average live longer.

Figure 14. Adults by household composition, by sex and broad age group, EU-27, 2019

(%)

Source: Eurostat (online data table: lfst_hhindws)

Figure 15. Women and men aged 65 and over who live alone 2019

(% of total women and men aged 65 and over)

Source: Eurostat (online data table: lfst_hhindws)

5. Mobile Europe

5.1    Immigrants and emigrants

Migration is the most volatile demographic component. It is not straightforward to measure and, during periods of faster change, the situation is even more complex, as migration becomes more unstable. For comparability, it is important to apply strict definitions. Following Eurostat’s definitions, immigration is the action by which a person establishes his or her usual residence in the territory of a Member State for a period that is, or is expected to be, of at least 12 months, having previously been usually resident in another Member State or a third country.

A total of 3.9 million people immigrated to one of the EU Member States during 2018, while at least 2.6 million emigrants were reported to have left an EU Member State. Within this, about 2.4 million people immigrated to EU from a country outside the EU and about 1.1 million people emigrated from EU to a country outside the EU 11 .

Figure 16. Immigrants from outside EU-27 and emigrants to outside EU-27, 2013-2018

(million persons)

Source: Eurostat (online data tables: migr_imm5prv, migr_imm12prv, migr_emi3nxt and migr_emi5nxt)

5.2    Age structure by citizenship 

Considering the population of all the Member States together, the non-nationals (persons not having the citizenship of the country they reside in) tend to be younger than the nationals (persons having the citizenship of the country they reside in). In 2019, the median age of the nationals residing in the EU-27 was 45, while the median age of non-nationals was 36. The largest age groups among non-nationals were around 30 years of age, while they were around 50 for the nationals.

Figure 17. Age structure by citizenship and sex, EU-27, 2019

(% of the nationals, or non-nationals)

Source: Eurostat (online data table: migr_pop2ctz)

The number of people residing in EU-27 without citizenship of a Member State in 2019 was 21.8 million, representing 4.9% of the EU-27 population. In addition, there were 13.3 million non-nationals living in one of the EU Member States with the citizenship of another EU Member State.

Figure 18. Share of non-nationals in the resident population, 2019

(%)

Note: For Cyprus and Malta, breakdown by citizenship not available

Source: Eurostat (online data table: migr_pop1ctz)

6. changing population

6.1Natural population change in the EU

The natural change in population (the gap between live births and deaths) in the EU-27 was narrowing from 1960s. From 2012 onwards, as the number of deaths surpassed the number of births, EU-27 experienced a natural population decrease. Since then, the gap between life births and deaths has been increasing and in 2018 deaths outnumbered births by 0.5 million at EU level.

During the period 1961-2018, the highest annual total for the number of live births in the EU-27 was recorded in 1964, at 6.797 million. From this relative high up to the beginning of the 21st century, the number of live births in the EU-27 declined at a relatively steady pace, reaching a low of 4.5 million in 1995. This was followed by an increase in the number of live births, with a high of 4.7 million children born in the EU-27 in 2008, in turn followed by reduction down to 2018 (4.2 million live births), which is the lowest value recorded. The downward trend is projected to continue, with under 4 million births in a year for most of the period until 2070.

In 2018, some 4.7 million persons died in the EU-27. The annual number of deaths is the highest observed over the previous five decades.

Figure 19. Live births and deaths, EU-27, 1961-2070

Observed (1961-2018) and projected (2019-2070) data (millions)

Source: Eurostat (online data tables: demo_gind and proj_19ndbi)

The 4.2 million children born in the EU-27 in 2018 correspond to a crude birth rate (the number of live births per 1 000 persons) of 9.5. For comparison, the EU-27 crude birth rate had stood at 10.5 in 1995, dropping from 16.4 in 1970. The crude death rate (the number of deaths per 1 000 persons) was of 10.5 in 2018.

Since 2012, the average crude rates in EU-27 were positive for the net migration and statistical adjustment and negative for the natural population change. In 2018, the crude rate for net migration and statistical adjustment stood at 2.6 per 1 000 persons, while the crude rate of natural population change was of -1.0. Their sum gives the crude rate of total change: 1.6 per 1 000 persons.

Population projections build on the observed trends of the crude rate for net migration and statistical adjustment over the last three decades. The annual average over the whole projection period stands at 2.3 migration events per 1 000 persons, which is close to the 2016-2018 average (2.2). While in 2016-2018 the net migration safeguarded the overall population growth, in less than a decade this value is projected to be insufficient to counterbalance the negative natural change. This would mean declining total population of the EU for most of the period until 2070. The population is projected to diminish to 424 million persons by 2070, a 5% reduction compared to the current value.

Figure 20. Population change by component, EU-27, 1960-2070

Observed and projected annual crude rates (per1 000 persons)

Source: Eurostat (online data tables: demo_gind and proj_19ndbi)

Figure 21. Crude rates of natural change and net migration, average 2014-2018

Ordered by ascending natural population change crude rate (per 1000 persons)

Source: Eurostat (online data table: demo_gind)

6.2Demographic balance in the Member States in 2018

The population of individual EU Member States on 1 January 2019 ranged from 0.5 million in Malta to 83 million in Germany. While the population of the EU-27 as a whole increased during 2018, the population of 10 EU Member States declined.

Table 5. Demographic balance, 2018

(thousands)

Source: Eurostat (online data table: demo_gind)

In 2018, population change diverged across the EU Member States: 17 Member States observed an increase in their respective populations, while the population fell in the remaining 10 Member States. Malta, Luxembourg, Ireland, Cyprus and Sweden recorded the highest population growth rates, with increases above 10.0 per 1 000 persons. Among these five EU Member States with the highest rates of population growth, the fastest expansion in population was recorded in Malta with an increase of 36.8 per 1 000 persons. The largest relative decreases in population were reported by Latvia (-7.5), Bulgaria and Croatia (both -7.1).

Table 6. Crude rates of population change, 2016-2018

(per 1 000 persons)

Source: Eurostat (online data table: demo_gind)

II: THE IMPACT OF DEMOGRAPHIC CHANGE – territorial differences

7. regional and local dimension

7.1    GDP (in PPS) per person employed

Differences at regional level are visible for example in different levels of GDP per person. 

Gross domestic product per person employed is a measure of labour productivity of the total economy of a country or region. Expressing GDP in purchasing power parities (PPS) compensates for cost of living differences between countries. Employment is measured at the place of work. Consequently, regional GDP per person employed is not distorted by commuting across regional boundaries.

Map 4. GDP (in PPS) per person employed by NUTS3 region, 2017

(EU-27 = 100)

7.2    Natural population change and net migration 12  

Map 5. Natural population change in NUTS3 regions, 2014-2019

Annual average change (per thousand)

(1)

Eurostat statistics in this Staff Working Document were extracted from the Eurostat database in May 2020.

(2)

In this Staff Working Document, the terms Europe and EU refer to EU-27 unless otherwise specified.

(3)

See Eurostat table demo_mlexpec

(4)

In April 2020, Eurostat published population projections based on 2019 data. At the time of projections’ production, the effects of the COVID-19 pandemic were not available, and they were not incorporated in the assumptions. For more information on Eurostat’s population projections, see: https://ec.europa.eu/eurostat/web/population-demography-migration-projections/population-projections-data .

(5)

Excluding Croatia and Latvia for which data for 2000 is not available

(6)

Healthy life years, also called disability-free life expectancy, is defined as the number of years that a person is expected to continue to live in a healthy condition. A healthy condition is defined as self-reported one without limitation in functioning and without disability. For more information on the concept and data, https://ec.europa.eu/eurostat/statistics-explained/pdfscache/1101.pdf . A similar indicator, Healthy life expectancy based on self-reported state of health describes how many years a person is expected to live in good self-perceived health. For the EU-27, the number of healthy life expectancy at birth in 2018, was estimated at 76.7 years for women and 73.2 for men (source: Eurostat online table hlth_silc_17 ). Cultural differences in self-reporting both health and disability can influence comparability of both indicators.

(7)

For an explanation of the NUTS classification (“Nomenclature of territorial units for statistics”), see: https://ec.europa.eu/eurostat/web/nuts/background  

(8)

For more information regarding the 2019 Revision of World Population Prospects, see: https://population.un.org/wpp/

(9)

In this context, children are considered economically dependent up to 24 years old.

(10)

‘Two adults (not a couple) or more adults’: this group includes all households of 2 or more persons where two adults that are not a couple can be found among members of the household, e.g., an economically independent adult child living with one or two parents parent, elderly person living with other members of the family, economically independent roommates)

(11)

For some immigrants the country of previous residence and for some emigrants the country of next residence are not known. For Cyprus, migration data includes UK in the composition of the EU. EU-27 data is not available for Cyprus.

(12)

See section 6 for definitions.

EUROPEAN COMMISSION

Brussels, 17.6.2020

SWD(2020) 109 final

COMMISSION STAFF WORKING DOCUMENT

ADDITIONAL FIGURES, MAPS AND TABLES ON THE KEY ASPECTS OF DEMOGRAPHIC CHANGE AND ITS IMPACT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

on the impact of demographic change

{COM(2020) 241 final}

Map 6. Net migration 1 in NUTS3 regions, 2014-2018

Annual average change (per thousand)

8. infrastructure and access to services

8.1    Accessibility to passenger flights

The indicator shows how many departures of passenger flights (average number per day) can be found at airports reachable within 90 minutes by road. If more than one airport can be reached, the number of available flights at these airports is summed. 2  

Map 7. Access to passenger flights by NUTS3 region, 2018

(average number of flights per day: population-weighted average number of flights per day, accessible within 90 minutes by road)

1.

9. URBAN, INTERMEDIATE AND RURAL REGIONS

The impact of demographic change depends on the speed of change. Slow population change, positive or negative, can usually be easily accommodated. Rapid change, however, generates adjustment costs.

Each region seeks to provide services and infrastructure to serve the needs of its population. When the population grows or declines, the services and infrastructure in that region need to be adjusted accordingly (e.g. transport infrastructure, housing, schools, hospitals). Whether the population is growing or shrinking, regions need to keep pace with the shifts in people’s needs:

•If population change unfolds relatively slowly, regions have time to adjust and costs are relatively limited

•In case of a sudden surge or drop in the population, the adjustment costs are likely to be higher

•Poor regions faced with rapid population change may struggle to find the necessary funding to adjust their infrastructure and services.

Map 8 shows the regions with rapid population growth in green and rapid population reductions in red. These regions have gained (or lost) 4% or more of their population between 2011 and 2019. The shade of green and red is determined by the level of GPD per head in 2017. Dark green and dark red regions have a GDP per head below 75% of the EU average, medium green and medium red have a GDP per head between 75% and the EU average. Regions in light green and light red have a GDP per head above the EU average.

The map shows that most of the rapidly shrinking regions have a low GDP per head, while most of the rapidly growing regions have a high GDP per head. This is also reflected in the population shares. Almost two thirds of the population in a rapidly shrinking region lives in a region with a low GDP per head, compared to 28% in the whole of the EU. Of the population in a rapidly growing regions, 83% lives in a region with a high GDP per head, compared to 46% in the whole of the EU.

The link between rapid population decline and GDP per head also has a strong urban-rural dimension. Of the population in poor regions with rapid population reductions, 55% live in a rural region and only 1% in an urban region, while for the total EU population 21% lives in a rural region and 40% in an urban region.

Map 8. GDP per person (PPS) 2017 by NUTS 3 regions with strong population increase or decline, 2011-2019

9.1    Population by level of regional GDP per head 

The chart classifies all NUTS 3 regions according to their level of GDP per head, relative to the EU-27 average. The regions with the lowest level of GDP/head (< 75% of the average) cover 29% of the Union’s population.

Figure 22. Population 2019 by level of regional GDP per head (NUTS3), EU-27, 2017

Source: Eurostat (online data table: demo_r_gind3 and nama_10r_3gdp)

9.2    Population in low-income regions by urban-rural typology

Amongst the NUTS-3 regions with the lowest GDP/head one finds 38% of population in rural regions.

Figure 23. Population in low-income regions by urban-rural typology, EU-27, 2018

Source: Eurostat (online data table: demo_r_pjangrp3 and nama_10r_3gdp)

9.3    Population in rapidly shrinking regions by urban-rural typology

Rural regions account for 44% of population of all regions that have experienced substantial population decline in recent years. Urban regions have been less prone to recent population decline.

Figure 24. Population in rapidly shrinking regions by urban-rural typology, EU-27, 2018

Source: Eurostat (online data table: demo_r_gind3) and JRC (ARDECO) data

9.4    Population in rapidly shrinking, low-income regions by urban-rural typology

Rural regions are overrepresented in terms of population when considering the group of rapidly shrinking regions (see Figure 24.) with a low GDP/head (see Figure 23). Almost no urban regions can be found in this group.

Figure 25. Population in rapidly shrinking, low-income regions by urban-rural typology, EU-27, 2018

Source: Eurostat (online data table: demo_r_pjangrp3 and nama_10r_3gdp) and JRC (ARDECO) data

9.5    Crude birth rate by urban-rural regional typology

Crude birth rate is the ratio of the total number of live births during the year to the average population in that year. Population is expressed in thousands, this being often referred to as ‘pro mille’. The crude birth rate is almost systematically higher in urban regions than in intermediate and rural regions, probably due to the differences in age structure of the population.

Figure 26. Crude birth rate, 2018

(per thousand residents, by urban-rural regional typology)

Source: Eurostat (online data table: demo_r_fagec3 and the urban-rural regional typology 3 )

EU-14 consists of the current Member States that joined the EU prior to 2004.

EU-13 consists of the Member States that joined the EU in 2004, 2007 and 2013.

9.6    Crude death rate by urban-rural regional typology

The crude death rate is the ratio of the total number of deaths during the year to the average population in that year. It is higher in rural regions than in intermediate regions, and lower in urban regions with few exceptions.

Figure 27. Crude death rate, 2018

(per thousand residents, by urban-rural regional typology)

Source: Eurostat (online data table: demo_r_magec3 and the urban-rural regional typology)

EU-14 consists of the current Member States that joined the EU prior to 2004.

EU-13 consists of the Member States that joined the EU in 2004, 2007 and 2013.

9.7    Total fertility rate by urban-rural regional typology

The total fertility rate is below 2.1 in all EU-27 Member States. Overall, it is higher in rural regions (1.62) than in intermediate (1.56) and urban regions (1.51).

Figure 28. Total fertility rate, 2018

(by urban-rural regional typology)

Source: Eurostat (online data table: demo_r_find3 and the urban-rural regional typology)

EU-14 consists of the current Member States that joined the EU prior to 2004.

EU-13 consists of the Member States that joined the EU in 2004, 2007 and 2013.

9.8.    Population change by urban-rural regional typology

In recent years EU-27 population has grown in urban and in intermediate regions (with 4.4 and 1.2 pro mille per year respectively), while it has somewhat declined in rural regions (-1.7 pro mille per year). Population growth in urban regions has been positive in most of the Member States, except in Greece, Latvia and Italy (in descending order of urban population reduction). Population in rural regions has shrunk in most Member States, but it has grown in Sweden, Ireland, Belgium, Austria, Denmark, Germany, Czechia and Slovakia (ranked by descending rural growth rates). In most cases, intermediate regions show a population evolution somewhere in between the values for urban and for rural regions.

Figure 29. Average annual population change between 1 January 2014 and 2019

(pro mille change by urban-rural regional typology) 

Source: Eurostat (online data table: demo_r_gind3 and the urban-rural regional typology)

EU-14 consists of the current Member States that joined the EU prior to 2004.

EU-13 consists of the Member States that joined the EU in 2004, 2007 and 2013.

Net migration in the EU-27 has been positive in urban, intermediate and rural regions. Nevertheless, negative rates of net migration (representing a larger outflow than inflow) can be seen in several Member States. For instance, rural regions in Latvia and Lithuania and intermediate regions in Estonia and Lithuania have experienced a far larger population outflow than inflow. In most of the Member States the net migration rate in urban regions is positive (except in Greece, France and Latvia in descending order of urban population reductions).

Figure 30. Average annual net migration rate 4 between 1 January 2014 and 2019

(pro mille change by urban-rural regional typology)

Source: Eurostat (online data table: demo_r_gind3 and the urban-rural regional typology)

EU-14 consists of the current Member States that joined the EU prior to 2004.

EU-13 consists of the Member States that joined the EU in 2004, 2007 and 2013.

Recent natural population change is slightly positive in urban regions and negative in intermediate and rural regions. In about half of the Member States natural population change is negative in urban regions. In most Member States natural change is lower in intermediate and in rural regions than it is in urban regions.

Figure 31. Average annual natural population change between 1 January 2014 and 2019

(pro mille change by urban-rural regional typology)

Source: Eurostat (online data table: demo_r_gind3 and the urban-rural regional typology)

EU-14 consists of the current Member States that joined the EU prior to 2004.

EU-13 consists of the Member States that joined the EU in 2004, 2007 and 2013.

10. CITIES, TOWNS AND SUBURBS, AND RURAL AREAS

This section shows data by degree of urbanisation, which classifies local administrative units, such as municipalities, into cities, towns and suburbs, and rural areas.

10.1    Employment rate by degree of urbanisation 5

The EU-27 employment rate for people aged 20-64 years in 2019 showed little difference by type of area. However, this average is the combined result of two distinct patterns we can observe when analysing employment rates by degree of urbanisation:

·in eastern parts of the EU and in Baltic countries, it was commonplace to find that employment rates for cities were the highest (for example, in Bulgaria, Lithuania or Poland), whereas their employment rates for rural regions tended to be considerably lower;

·in western Member States, the picture was often reversed, insofar as the highest employment rates were usually recorded for people living in rural areas (for example, in Belgium, Germany or Austria), whereas their lowest employment rates were recorded in cities.

Figure 32. Employment rate, 2019

(%, people aged 20-64 years in employment as a share of the total population aged 20-64 years, by degree of urbanisation)

Source: Eurostat (online data table: lfst_r_ergau)

10.2    Unemployment rate by degree of urbanisation

In 2019, the lowest unemployment rates — among types of areas — were recorded in rural areas in 12 of the EU Member States — including France, Austria and Germany. By contrast, there were eight EU Member States — including Lithuania, Bulgaria and Slovakia — where the highest unemployment rates were recorded for those living in rural areas. The gap in unemployment rates between those living in cities and rural areas widened to above 5 percentage points in Bulgaria and Austria, showing that wide disparities by degree of urbanisation are visible in both the eastern and western parts of the EU.

Figure 33. Unemployment rate, 2019

(%, unemployed people aged 15-74 years as a share of the total labour force aged 15-74 years, by degree of urbanisation)

Source: Eurostat (online data table: lfst_r_urgau)

(¹) Rural areas: not available.

10.3    NEET by degree of urbanisation

The share of young people aged 18-24 years who were neither in employment nor in education or training (NEET) may be expressed relative to the total population of the same age (18-24 years). The NEET rate was highest in rural areas and lowest in cities. In 18 Member States the lowest rate was registered in cities. This figure may reflect, to some degree, the concentration of educational establishments in cities. The NEET rate ranged from a low of 4.1 % in the cities of Slovakia, up to over 30 % in the rural areas of Bulgaria and Greece.

Figure 34. Young people neither in employment nor in education and training (NEETs), 2019 (%, share of people aged 18-24 years, by degree of urbanisation)

Source: Eurostat (online data table: edat_lfse_29)

(¹) Rural areas: not available.

(²) Cities: low reliability.

(³) Cities: not available.

10.4    Early leavers from education and training by degree of urbanisation

The share of early leavers from education and training is defined as the proportion of 18–24 year-olds with at most a lower, secondary level of education and who are no longer in further education or training. An analysis of early leavers from education and training by degree of urbanisation reveals that across the EU-27, the highest share of early leavers in 2019 was recorded among young people living in towns and suburbs (11.2 %), while the lowest share was recorded for those living in cities (9.1 %). The majority of the EU Member States recorded their lowest rates in cities.

Figure 35. Early leavers from education and training, 2019

(%, share of people aged 18-24 years, by degree of urbanisation)

Source: Eurostat (online data table: edat_lfse_30)

(¹) Rural areas: not available.

(²) Rural areas: low reliability.

(³) Towns and suburbs: low reliability.

(⁴) Cities: low reliability.

(⁵) Cities, Towns and suburbs and Rural areas: low reliability.

(⁶) Cities and Towns and suburbs: low reliability.

(⁷) Cities: not available, Rural areas: low reliability.

(⁸) Cities: not available.

10.5    At-risk-of-poverty rate by degree of urbanisation

In 2018, there were wide disparities across the EU Member States regarding areas where the at-risk-of-poverty rate after deducting housing costs tended to be concentrated. The share of the population that was at risk of poverty after deducting housing costs was particularly high among people living in cities in much of western Europe, while in eastern and southern parts of the EU, it was more common to find the highest incidences among those living in rural areas.

Figure 36. At-risk-of-poverty rate after deducting housing costs, 2018

(%, share of total population, by degree of urbanisation)

Source: Eurostat (online data table: ilc_li48)

(¹) Estimated

(²) Rural areas: not available.

10.6    Median equivalised income by degree of urbanisation

In 2018, median equivalised net income expressed in purchasing power standards (PPS), in order to take account of the differences in the cost of living across countries, analysis by degree of urbanisation shows that income levels were, on average, higher for those living in towns and suburbs and cities, while the median income in rural areas was lower. In 20 Member States, the highest median income was recorded in cities.

Figure 37. Median equivalised net income, 2018

(Purchasing power standard (PPS), by degree of urbanisation)

Source: Eurostat (online data table: ilc_di17)

(¹) Estimated

(²) Rural areas: not available.

10.7    Tertiary educational attainment by degree of urbanisation

In 2019, more than 40 % of the EU-27 working-age population (defined here as those aged 25‑64) living in cities had a tertiary level of educational attainment. This was considerably higher than the corresponding shares recorded among those living in towns and suburbs (27.7%) or rural areas (22.2 %). The proportion of working-age population with a tertiary level of educational attainment was higher among those living in cities, compared with those living in rural areas, in all EU Member States except Malta.

Figure 38. Tertiary educational attainment, 2019

(%, share of people aged 25-64 years, by degree of urbanisation)

Source: Eurostat (online data table: edat_lfs_9913)

10.8    People who perceive their health as good health by degree of urbanisation

In 2018, more than two thirds of the adult population perceived their own health to be good or very good. This share was highest for people living in cities (70.3 %), followed by people living in towns and suburbs (69.2 %) and then people living in rural areas (65.6 %). Note that self-perceived health status is strongly related to age, and therefore the analysis of health status by degree of urbanisation may reflect, to some degree, differences in age structures. A closer analysis among the EU Member States reveals that a higher proportion of people living in cities perceived their own health as good or very good in a majority (19) of the EU Member States. By contrast, in Belgium and the Netherlands, people living in the rural areas were more likely to perceive their own health to be good or very good. The gap between cities and rural areas was particularly pronounced (by more than 10 percentage points) in Bulgaria, Lithuania and Portugal.

Figure 39. People who perceive their own health as good or very good, 2018

(%, share of population aged ≥16 years, by degree of urbanisation)

Eurostat (online data table: hlth_silc_18)

(¹) Estimated

(²) Rural areas: not available.

10.9    Proportion of population born outside EU

Migration from non-EU countries has led to a large share of non-EU foreign-born residents in some EU Member States. Even within a country, the distribution of the population born outside of the EU is far from being geographically dispersed. Their share in the adult population (aged 15 years or over) in cities is (11.7%) - three times higher than that of rural areas (3.7%). In almost all EU Member States, the highest proportion of population born outside the EU were recorded in cities.

Figure 40. Proportion of population born outside the EU, 2019

(%, share of population aged 15 years or over, by degree of urbanisation)

Source: Eurostat (online data table: lfst_r_pgauwsc)

(¹) Rural areas: not available.

(²) Towns and suburbs: not available, Rural areas: low reliability.

(³) Cities: low reliability, Towns and suburbs and Rural areas: not available.

10.10    Proportion of population born in another EU Member State by degree of urbanisation

Adult residents (aged 15 years or over) born in a different EU Member State are far less concentrated in cities than those born outside the EU. Nevertheless, similar to non-EU born migrants, in most Member States (20 Member States), the cities, as compared to towns, suburbs and rural areas, attract more adult residents born in a different Member State.

Figure 41. Proportion of population born in another EU Member State, 2019

(%, share of population aged 15 years or over, by degree of urbanisation)

Source: Eurostat (online data table: lfst_r_pgauwsc)

(*) The values for Luxembourg are: Cities: 59%, Towns and suburbs: 37%; Rural areas: 34%

(¹) Rural areas: low reliability.

(²) Towns and suburbs: low reliability.

(³) Cities: low reliability, Towns and suburbs and Rural areas: not available.

(⁴) Romania: Data not available.

(1)

Including statistical adjustment

(2)

  http://ec.europa.eu/regional_policy/en/information/publications/regional-focus/2013/measuring-accessibility-to-passenger-flights-in-europe-towards-harmonised-indicators-at-the-regional-level  

(3)

Urban-rural regional typology: https://ec.europa.eu/eurostat/web/rural-development/methodology

(4)

Net migration includes statistical adjustment: i.e. it is calculated as the difference between total population change and natural population change.

(5)

Employment and unemployment rates are based on 2019 data, before the outbreak of COVID-19 pandemic.