Demographic scenarios
2010-2030
Corina Huisman 1
Joop de Beer1
Rob van der Erf1
Nicole van der Gaag1
Dorota Kupiszewska 2
NEUJOBS Working Paper D 10.1
Revised version, March 2013
Abstract
This paper describes the assumptions underlying the demographic projections that will be
part of the tough and friendly NEUJOBS scenarios. The demographic scenarios project the
population by age, sex, type of region (urban, rural, intermediate), level of educational
attainment and household position for all EU countries until 2030. In the tough scenario the
levels of fertility, life expectancy and net migration will be lower than in the friendly
scenario due to poor economic conditions. As a consequence population growth will be
lower in the tough scenario than in the friendly scenario. In both scenarios population
ageing will be the main demographic trend across Europe. The dependency ratio will
increase. In the friendly scenario the size of the working age population will be larger than
in the tough scenario, but the share of the working age population in the total population
will decline in both scenarios. In the tough scenario the progress in education will stagnate.
Working Papers present work being conducted within the NEUJOBS
research project, which analyses likely future developments in
European labour markets.
The views expressed in this report are those of the authors and do
not necessarily represent any institution with which they are
affiliated.
1
2
Netherlands Interdisciplinary Demographic Institute (NIDI) The Hague, The Netherlands
Central European Forum for Migration and Population Research (CEFMR) Warsaw, Poland
Table of contents
Figures ............................................................................................................................................. 4
Tables .............................................................................................................................................. 5
1 Introduction ................................................................................................................................ 7
2 The global SET scenarios............................................................................................................. 9
3 Translation from global SET scenarios to demographic scenarios ........................................... 10
4 Eurostat scenario: EUROPOP2010............................................................................................ 13
5 Assumptions underlying NEUJOBS scenarios ........................................................................... 13
5.1
5.2
5.3
5.4
5.5
5.6
Fertility ........................................................................................................................................... 13
Life expectancy .............................................................................................................................. 16
International migration .................................................................................................................. 20
Urbanisation................................................................................................................................... 23
Level of educational attainment .................................................................................................... 24
Household position ........................................................................................................................ 27
6 Results for Germany ................................................................................................................. 32
7 References ................................................................................................................................ 40
3
Figures
Figure 1
Observed total fertility rates for a selection of countries .......................................................................... 14
Figure 2
Observed and projected total fertility rates according to the friendly scenario........................................ 14
Figure 3
Observed and projected total fertility rates according to the tough scenario .......................................... 15
Figure 4
Base models for female life expectancies .................................................................................................. 17
Figure 5
Life expectancies in Germany .................................................................................................................... 19
Figure 6
Net migration numbers for Germany (x 1,000).......................................................................................... 22
Figure 7
Percentage of the population living in an urban region, Germany, 1990, 2010 and 2030 ........................ 24
Figure 8
Men by level of educational attainment, Germany, 2010 ......................................................................... 25
Figure 9
Women by level of educational attainment, Germany, 2010 .................................................................... 25
Figure 10 Men by level of educational attainment, Germany, 2010 and 2030, Fast Track scenario ......................... 26
Figure 11 Women by level of educational attainment, Germany, 2010 and 2030, Fast Track scenario ................... 26
Figure 12 Men living alone by level of educational attainment, Germany, 2010 ...................................................... 29
Figure 13 Women living alone by level of educational attainment, Germany, 2010................................................. 29
Figure 14 Men living with partner without children by level of educational attainment, Germany, 2010 ............... 29
Figure 15 Women living with partner without children by level of educational attainment, Germany, 2010 .......... 30
Figure 16 Men living with partner with children by level of educational attainment, Germany, 2010 ..................... 30
Figure 17 Women living with partner with children by level of educational attainment, Germany, 2010 ............... 30
Figure 18 Men living without partner with children by level of educational attainment, Germany, 2010 ............... 31
Figure 19 Women living without partner with children by level of educational attainment, Germany, 2010 .......... 31
Figure 20 Total population according to different scenarios, Germany, 2010-2030 (millions) ................................. 32
Figure 21 Total working age population according to different scenarios, Germany, 2010-2030 (millions)............. 32
Figure 22 Age structure in 2030 according to different scenarios, Germany, 2010-2030 (millions) ......................... 33
Figure 23 Old age dependency ratio according to different scenarios, Germany, 2010-2030 .................................. 33
Figure 24 Total dependency ratio according to different scenarios, Germany, 2010-2030 ...................................... 34
Figure 25 Population pyramid Germany, 2010 (%) .................................................................................................... 35
Figure 26 Population pyramid Germany, 2030, friendly scenario (%) ....................................................................... 35
Figure 27 Population pyramid Germany, 2030, tough scenario (%) .......................................................................... 35
Figure 28 Population pyramid, predominantly rural regions, Germany, 2010 (%) .................................................... 36
Figure 29 Population pyramid, predominantly rural regions, Germany, 2030, friendly scenario (%) ....................... 36
Figure 30 Population pyramid, predominantly rural regions, Germany, 2030, tough scenario (%) .......................... 36
Figure 31 Population pyramid, intermediate regions, Germany, 2010 (%) ............................................................... 37
Figure 32 Population pyramid, intermediate regions, Germany, 2030, friendly scenario (%)................................... 37
Figure 33 Population pyramid, intermediate regions, Germany, 2030, tough scenario (%) ..................................... 37
Figure 34 Population pyramid, predominantly urban regions, Germany, 2010 (%) .................................................. 38
Figure 35 Population pyramid, predominantly urban regions, Germany, 2030, friendly scenario (%) ..................... 38
Figure 36 Population pyramid, predominantly urban regions, Germany, 2030, tough scenario (%) ........................ 38
Figure 37 Population pyramid by educational level, Germany, 2010 (%) .................................................................. 39
4
Figure 38 Population pyramid by educational level, Germany, 2030, friendly scenario (%) ..................................... 39
Figure 39 Population pyramid by educational level, Germany, 2030, tough scenario (%) ........................................ 39
Tables
Table 1
Total fertility rates observed and projected by 2030 according to different scenarios ............................. 16
Table 2
Life expectancy at birth for men observed and projected by 2030 according to different scenarios ....... 19
Table 3
Life expectancy at birth for women observed and projected by 2030 according to different scenarios .. 20
Table 4
Net migration numbers observed and projected by 2030 according to different scenarios (x 1,000) ...... 23
5
1
Introduction
This report describes the assumptions about future changes in fertility, mortality, international and internal
migration, level of educational attainment and household position that underlie the demographic
projections for the tough and friendly NEUJOBS scenarios. The demographic scenarios project the
population by age, sex, urbanisation, level of educational attainment and household position for the period
2010-2030 for all EU countries. This report summarises the main results for Germany. The results for all EU
countries are available in a separate database.
For the tough and friendly scenarios two different demographic projections are calculated. Similar to the
global tough scenario the demographic tough scenario for Europe assumes lower economic growth than
the friendly scenario. Since adverse economic conditions have a negative impact on fertility, we assume
that in the tough scenario the total fertility rate will be lower than in the friendly scenario. Cuts in the
public budget for health care can be expected to have an adverse effect on life expectancy. Moreover we
can assume that stress related with poor economic conditions will have a negative effect on health. Thus
we assume that in the tough scenario the increase in life expectancy will be lower than in the friendly
scenario. Lack of jobs will have a downward effect on immigration, and a positive effect on emigration, thus
resulting in low net migration in the tough scenario. In the tough scenario the effects of climate change
may lead to an increase in the number of displaced persons, but since most displaced persons can be
expected to remain in their region, the effect on migration to Europe can be expected to be relatively small.
Moreover, in a situation of poor economic developments immigration policies will be strict, whereas in a
situation with relatively high economic growth, labour migrants may be welcome. Thus net migration to
Europe is assumed to be higher in the friendly scenario than in the tough scenario.
The assumptions on fertility, mortality and international migration determine the population size and age
structure at the national level. At the regional NUTS 2 level we look at the distribution of the population
across rural, intermediate and urban regions. The most common settlement pattern across the EU-27
countries is urbanization of the young (about 15 to 40 years of age) together with peri- or counterurbanization of the higher age groups. In absolute terms, many more migrants decide to move into urban
regions than to move out of these regions, to a lesser extent the same is true for intermediate regions,
while rural regions may lose people. Rural-urban migration depends on employment opportunities and
residential preferences. Since we may assume that in the tough scenario job opportunities will be scarce in
both urban and rural regions, there is no reason to assume that the urban-rural migration pattern will be
different between the tough and friendly scenarios. Nevertheless we may assume that the percentages of
the population living in urban and rural regions will change in the future. First, in most countries
immigrants tend to move to urban rather than rural regions. Thus, as we assume higher immigration in the
friendly scenario than in the tough scenario, more immigrants will move to urban regions. Second,
residential preferences develop during the life course: young people move to urban regions to study and
work, whereas families with young children tend to move from urban centres to peri-urban environments,
and older people move to rural regions. Therefore due to the ageing of the population, the distribution
between rural and urban NUTS 2 regions will slightly change in favour of the urban regions in both
scenarios.
7
In addition to projecting the population by age and sex, we also project the population by educational level
and household position. Young generations tend to have a higher level of educational attainment than
older generations. However, poor economic conditions may lead to budget cuts for educational facilities.
Thus in the tough scenario we assume that the increase in the level of educational attainment will stagnate.
The population by age, sex and level of educational attainment is further distinguished by household
position. We distinguish six household positions: living at parental home, alone, with a partner without
children, with a partner with children, lone parent, and ‘other’. The age at which children leave their
parental home to form their own household is affected by economic opportunities. If the economy
develops poorly, this will have a downward effect on the formation of new households by young adults.
Furthermore the relatively low level of fertility in the tough scenario will have a downward effect on the
number of families with young children. Since living alone is more costly than living with a partner or living
in another type of multi-persons household, one may expect that in the tough scenario the number of
people living alone will be smaller than in the friendly scenario. These effects are reinforced by changes in
the distribution of the population by educational attainment. Since the share of people living alone among
highly educated categories is higher than among people with lower education, the number of households in
the friendly scenario will be higher than in the tough scenario.
The demographic scenarios will project the population for the period 2010-2030. The demographic
projections will be the basis for labour force scenarios. The calculation of the demographic scenarios
consists of four steps.
1.
2.
3.
4.
A cohort component model will be used for the projection of the population by age and sex at the
national level for all EU countries on the basis of assumptions about the future levels of fertility, life
expectancy and net international migration.
The projection of the population by age, sex, and type of region (urban, rural, intermediate) will be
based on a new urban-rural typology of NUTS 2 regions, developed in WP8. These projections are
based on assumptions about net internal migration flows between urban, rural and intermediate
regions.
The projection of the population by age, sex and level of educational attainment will be based on
data from the LFS and on assumptions underlying educational scenarios developed by IIASA (KC et al.,
2010). We will make assumptions about the distribution of the population by age and sex across
three categories of educational attainment: low, medium and high.
The projection of the population by age, sex, level of educational attainment and household position
will be based on data from the LFS. Six household positions will be distinguished: (1) living at the
parental home, (2) living alone, (3) living with a partner, without children, (4) living with a partner,
with children, (5) single parent and (6) other. For each level of educational attainment the share of
the population by age and sex in each of the household positions will be calculated. In the future
these shares are assumed to be constant within each education category. This implies that the future
distribution of the population by household position will differ across the scenarios as a result of
differences in the level of educational attainment.
8
For calculating the national demographic scenarios the so-called cohort component method is used. The
cohort component technique uses the components of demographic change to project population growth.
The technique projects the population by age groups and sex. This projection method is based on the
components of demographic change: births, deaths, and migration. The basic idea is that the population in
year t+1 can be projected by the so-called demographic equation:
Population(t+1) = Population(t) + Births(t) - Deaths(t) + Net Migration(t)
Given the size of a population at the beginning of year t, the population at the beginning of year t+1 can be
projected on the basis of assumptions about the number of deaths during year t, the number of births
during year t and the number of (net) migrants during year t. In the cohort component method populations
are distinguished by sex and age, and each subgroup (of sex and age) is exposed to age- and sex-specific
chances to a demographic event (giving birth, dying and migrating). The assumptions of the future numbers
of births and deaths are based on assumptions about age- and sex-specific fertility and mortality rates
respectively.
Across Europe population ageing will be the main demographic trend in the next decades. This trend will be
manifest in both the tough and friendly scenarios. One main consequence of population ageing will be that
the growth of the working age population will decline or will become negative and that the dependency
ratio (the ratio between the active and non-active age groups) will increase. The decrease in the growth of
the working age population will have a downward effect on structural economic growth (given the level of
labour force participation and labour productivity), whereas the increase in the dependency ratio will lead
to an increase in costs of pensions, health care, and long-term care (given the level of age-specific per
capita expenditures).
2
The global SET scenarios
In this section the assumptions for the different components of population projections are specified,
following the description of two global scenarios. These scenarios are formulated in NEUJOBS-WP1document: “SOA Report: Socio-ecological transitions (review), WP: Global scenarios and key assumptions”.
In that report it is argued that a next (or on-going) socio-ecological transition may be expected to have farreaching implications, not only for production and consumption patterns, but also for many other features
of society. There is ample evidence provided by global change research that human activity caused and
causes major changes in the functioning of natural systems on every spatial scale, from local to global, and
is transforming the earth system at an increasing pace. Such changes are now being accelerated by the ongoing process of industrial transformation in very populous, emerging economies. The further expansive
continuation of the industrial socio-metabolic regime for a then majority of the world seems biophysically
not feasible and threatens to erode humanity’s natural base. An exhaustion of cheap fossil fuels and a
number of other natural resources, and an increasing volatility of the climate system, are likely to happen,
and in some case already happening. This is what the authors sketch as global megatrends in natural
conditions, the ecological part of an already on-going next socio-ecological transition (SET).
The authors see elements of the social part of a next socio-ecological transition happening, related to social
and technical achievements generated by the last transition, what they term global, societal megatrends.
9
The three most important elements of a next SET they identify as a (continuation of) global demographic
transition, the on-going shift in the economic and political centres of gravity worldwide, the growing use of
information and communication technologies and the related new forms of knowledge sharing. The
authors do not know how fast and how radical these megatrends will evolve, they therefore distinguish
between tough and friendly variants for each megatrend and use these as global framework scenarios for
the European option space.
-
-
3
A friendly future includes rather moderate changes which are less challenging for European policy
making. It focuses on incremental global changes in the lower ranges of change found in the
literature.
The sketch of a tough global future is based on still quite likely but rather severe changes which
would be highly challenging for European policy making, using the higher ranges of change found in
literature, including possible abrupt changes.
Translation from global SET scenarios to demographic scenarios
The point of departure for the demographic scenarios is the socio-ecological transition as described in the
report ‘Socio-ecological transitions: definition, dynamics and related global scenarios’. In this report, global
SET scenarios are sketched that mark the starting point for more detailed analyses and scenario and
modelling efforts of the future labour force in Europe. Six global megatrends are distinguished: energy
transitions, challenges to resource security, climate change, population dynamics, shifting economic
centres of gravity, and growing ICT use. In the tough scenario these trends will impact upon Europe in a
“tough” fashion, i.e. this scenario assumes rather severe changes which would be highly challenging for
European policy making. For example, the tough scenario assumes that the share of the EU in the world
economy will decline. GDP growth in the EU will be relatively low. The demographic megatrends in the SET
scenario refer to the world population. The tough scenario assumes high population growth due to high
levels of fertility in developing countries. Furthermore migration pressure will be higher due to climate
change. In contrast, the friendly scenario assumes higher GDP growth in the EU, moderate population
growth in developing countries and stagnation of migration to Europe. This working paper develops two
population scenarios for EU countries on the basis of assumptions about the effects of the global
megatrends on the levels of fertility, life expectancy, migration, urbanisation, education and household
dynamics in Europe.
The level of fertility is adversely affected by poor economic conditions. When there is less job security,
when the risks of unemployment increase, when purchasing power develops poorly and when it becomes
increasingly difficult to finance buying a new house, young couples will tend to postpone the decision to
start a family and couples having one or two children hesitate to have another child. To some extent these
effects will be temporary, and for a number of couples the postponed children will be born at a later time.
But for other couples the family size will remain smaller than it would have been under favourable
economic conditions. One reason is that fecundity will decline when women become older. Another reason
is that delay of childbearing to older ages increases the risk of a break-up of the relationship before children
are born. One additional reason may be that couples may regard themselves to be “too old” to have and
raise children. Furthermore poor economic conditions may lead to cuts in public expenditures for child care
and cutbacks in possibilities for parental leave. Thus we assume that in the tough scenario the fertility level
will be lower than in the friendly scenario.
10
The increase in life expectancy depends on progress in medical care and access to health care. Thus budget
cuts will have an adverse effect on health and life expectancy. In addition, progress in medical technology
may slow down. Furthermore cutting down health promotion activities will have a negative effect on life
style, e.g. smoking, drinking and unhealthy diets. In addition lack of job opportunities may cause stress
which has detrimental effects on health. Cuts in budgets for disease prevention (e.g. screening, early
diagnoses) may lead to an increase in the prevalence of diseases and have a negative impact on life
expectancy. Less spending on health protection (e.g. safety measures, vaccines) may lead to an increase in
premature death. Furthermore deterioration of the environment may lead to an increase in the prevalence
of respiratory illness. Thus in the tough scenario progress in life expectancy will be smaller than in the
friendly scenario.
International migration is affected by differences in wealth and job opportunities between sending and
receiving countries. Poor economic conditions in Europe have a downward effect on labour migration from
other regions. In the tough scenario the demand of labour will be smaller than in the friendly scenario.
Moreover immigration policies will be more strict. Furthermore losing a job may be an incentive for
migrants to move back to their country of origin or to move to another region where there are better
opportunities. Thus the tough scenario will have a smaller number of (labour) migrants than the friendly
scenario. In contrast, climate change may have an upward effect on displaced persons in the tough scenario.
Since most displaced persons outside Europe can be expected to remain in their region, the effect on
migration to Europe can be expected to be relatively small. Similarly, persons affected by floods or
droughts in Europe can be expected to move to another region in their country rather than to move to
another European country. One reason is that in the tough scenario unfavourable economic conditions can
be expected to lead to strict immigration policies. Thus the upward effect of climate change on
international migration in the tough scenario is assumed to be smaller than the downward effect of the
economy on labour migration. In sum, net migration is assumed to be smaller in the tough scenario than in
the friendly scenario.
The distribution of the population across urban and rural regions depends on the size of urban-rural
migration, the choice of place of residence by immigrants and differences in the numbers of births and
deaths between urban and rural regions. Rural-urban migration depends on employment opportunities and
residential preferences. Both migration motives change over the life course. In most countries the general
pattern is that young people move from rural to urban regions for reasons of study and work. Most of them
remain in the city when they enter into a relationship. However, when they have children many choose to
move from urban centres to suburban or rural areas. These life course patterns are cause of age differences
in the percentage of people living in urban regions. In most countries the percentage of the population
living in urban areas among people in their twenties and thirties exceeds the percentage among people in
their fifties and sixties. These long-run, structural trends are relatively large compared to the temporary
effects of the economic business cycle. If the economy develops poorly and there is a lack of job
opportunities, this may have a downward effect on the tendency to move because of work reasons.
However, to the extent that poor economic developments have an effect on both urban and rural regions,
this will have only a limited impact on the distribution of the population across urban and rural regions in
comparison with the structural effects. Climate change may cause differences in internal migration patterns
between the tough and friendly scenarios. However, we assume that the effects on migration between
urban and rural regions are relatively small compared with structural changes due to cohort and life course
effects, and the effects of international migration. Part of climate change driven internal moves can be
expected to take place within the same NUTS2 region, for instance in Andalusia in Spain from inland desert11
like regions to coastal cities. Moreover, even in regions where big changes are anticipated, such as very
cold and mountainous regions, these changes will affect relatively small populations only and part of them
may be expected to move from one rural to another rural region. In addition mitigation measures to be
taken in the next decades in case regions may have to cope with territorial challenges may be expected to
reduce the impact on migration (Van der Gaag et al., 2012). Thus we expect that climate changes will not
lead to major differences in the distribution of the population between urban and rural regions between
the tough and friendly scenarios. In summary, we assume that in both the tough and friendly scenarios the
long-run structural changes prevail and thus that the differences between both scenarios in the
percentages of the population living in urban, intermediate and rural regions are relatively small.
The percentage of young people following tertiary education is higher than it was a number of years ago. As
a result the level of educational attainment of young generations exceeds that of older generations. As a
result of these cohort effects the level of educational attainment will increase in the long run, even if there
would not be a further increase in the participation in tertiary education among new generations. Whether
or not there will be a further increase among young generations depends on the future economic situation.
Poor economic conditions may lead to budget cuts for educational facilities. Furthermore budget cuts may
have a negative impact on programmes for lifelong learning. Thus in the tough scenario we assume that the
increase in the level of educational attainment will stagnate, whereas in the friendly scenario we assume
further progress.
Changes in the distribution of the population across household types mainly depends on five main types of
life course transitions: from leaving the parental home to living alone or living with a partner, entry into a
relationship, having children, break up of a relationship and becoming widow(er). The timing of these
transitions vary between generations. One main cause of the differences across generations is the level of
educational attainment. Young persons with a high level of educational attainment tend to postpone living
with a partner and having children. As a result the percentage of young people living alone among higher
educated groups is higher than among people with low educational levels. In addition the percentage of
couples without children among young people with high education is higher than among lower educated
couples. In contrast, among couples in their forties and fifties, the percentage of highly educated couples
with children is higher. Since people with a low educational level marry at younger age and couples married
at young age have a higher risk of divorce, the share of mothers living without a partner among people with
low educational levels is higher than among women with high education. Since the level of educational
attainment among young generations is higher than among older generations, these differences in
household dynamics across educational groups lead to differences in the household situation across
generations and thus to structural changes in the distribution of the population across types of households.
Economic conditions mainly effect the timing of the life course transitions rather than whether or not they
will occur. For example, adverse economic development may cause young people to stay longer in the
parental home. This will lead to a decline in the percentage of young people living alone. Poor economic
conditions may cause young people to wait longer before they will live together with their partner. And
financial difficulties which make it more difficult to find a new dwelling may have led to postponing the
decision to break up a relationship. Thus in the tough scenario we assume that the percentage of people
living alone will be lower than in the friendly scenario. Since in the tough scenario the level of fertility is
lower than in the friendly scenario, the percentage of young couples without children exceeds that in the
friendly scenario. The opposite is true for young couples with children. Since life expectancy in the friendly
scenario is higher than in the tough scenario, the number of older people living alone in the friendly
scenario is higher.
12
4
Eurostat scenario: EUROPOP2010
For the NEUJOBS population projections, we will use the assumptions underlying the national population
projections made by Eurostat as our point of reference. These projections are called EUROPOP2010, and
were released in April, 2011. EUROPOP2010 covers the period 2010-2060 and all 27 EU Member States. For
the NEUJOBS population projections we will formulate two different scenarios, the tough and friendly
scenarios, using EUROPOP2010 as a reference.
As was the case with the EUROPOP2008 demographic projection (Giannakouris, 2010), the EUROPOP2010
was made using a ‘convergence’ approach. This means that the key demographic determinants are
assumed to converge over the very long-term. These demographic determinants are: (1) the fertility rate;
(2) the mortality rate and (3) the level of net migration. As far as fertility and mortality are concerned, it is
assumed that they converge to that of ‘forerunners’ (EUROPOP2010).
In short, fertility rates are assumed to converge to levels achieved by Member States that are considered to
be ‘forerunners’ in the demographic transition. Life expectancy increases are assumed to be greater for
countries at lower levels of life expectancy and smaller for those at higher levels, thus following convergent
trajectories. In each Member State, immigration and emigration flows assumed to converge, taking also
into account the changes in the national age structures (EUROPOP2010).
5
Assumptions underlying NEUJOBS scenarios
5.1
Fertility
Past fertility rates
Total fertility rates have declined sharply in the EU Member States since the post-war “baby boom” peak
above 2.5 in the second half of the 1960s, to below natural replacement level of 2.1. The trend of falling
fertility rates differed across countries in size and timing. A more recent phenomenon, over the last decade,
indicates a reverse in the declining trend: on average in the EU, fertility rates have increased since 2000. In
particular, increases are noted in almost all Member States, with total fertility rates above 1.8 in Belgium,
Denmark, Ireland, France, Sweden and the UK. Sweden is considered a forerunner, with early and relatively
strong increases in fertility rates. By contrast, fertility rates have continued to fall in Luxembourg, and
Portugal while in Cyprus and Malta it has increased since 2005 (EUROPOP2010).
EUROPOP2010 fertility rates
Eurostat assumes a modest recovery in the total fertility rate (the average number of births per woman
over her lifetime) for the EU, from 1.59 births per woman in 2010 to 1.64 by 2030 and 1.71 by 2060. In all
countries, the fertility rate would remain below the natural replacement rate of 2.1 births per woman that
is needed in order for each generation to replace itself. The fertility rate is projected to increase over the
projection period in nearly all Member States, with the exception of Ireland, France, Sweden and the UK
(though remaining above 1.9), and in Belgium, Denmark and Finland it is projected to remain stable. As a
result of the convergence assumption, the largest increases in fertility rates are projected to take place in
Latvia, Hungary and Portugal, which have the lowest fertility rates in the EU in 2010. The increase is
projected to occur gradually, with fertility rates in these countries approaching but not reaching the current
EU average fertility rate in 2060 (EUROPOP2010).
13
NEUJOBS fertility rates
Considering past trends in fertility, and especially the reverse in fertility rates the last decade, it seems
highly unlikely that fertility rates will decline in the future (irrespective of the variant). We therefore
assume different trajectories of growth for the two different variants. Currently, the Scandinavian countries
have among the highest fertility rates in the EU. The total fertility rate in Sweden is 1.98 for example (in the
year 2010). Figure 1 shows how the total fertility rates developed for a selection of countries in the last 40
years.
Figure 1
Observed total fertility rates for a selection of countries
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1970
1975
Bulgaria
1980
1985
Germany
1990
Hungary
1995
Portugal
2000
2005
Finland
2010
Sweden
For the friendly scenario we assume that the fertility rate in Sweden (a forerunner) will slightly increase to a
value of 2.0 and that all countries will see their total fertility rate increase in a linear way from the latest
observed (2010) value, to the Swedish value of 2.0 (a convergence of trends). The linear path is chosen in
such a way that the difference with the Swedish total fertility rate by 2030 has halved as compared to the
currently observed difference. For the few countries that have slightly higher values than Sweden (Ireland
and France), it is assumed they remain stable at their current observed value. Figure 2 shows how the
fertility rates will develop from 2010 onwards for the same selection of countries.
Figure 2
Observed and projected total fertility rates according to the friendly scenario
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1970
1975
1980
Bulgaria
1985
1990
Germany
1995
2000
Hungary
2005
2010
Portugal
2015
2020
Finland
14
2025
Sweden
2030
In the tough scenario it is assumed that the countries will not change their reproductive patterns. Current
total fertility rates are maintained throughout the projection period (figure 3).
Figure 3
Observed and projected total fertility rates according to the tough scenario
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1970
1975
1980
Bulgaria
1985
1990
Germany
1995
2000
Hungary
2005
2010
Portugal
2015
2020
Finland
2025
2030
Sweden
Please note that due to relatively short projection period (2010-2030, i.e. 20 years), fertility assumptions
will not have a large impact on the size of the working age population. Only few of the children born during
the projection period will reach the labour force age during that same projection period. However, a
change in fertility rates does have an impact on total dependency ratios (the ratio between on the one
hand the number of people aged 65 or over plus the population younger than 15 and on the other hand the
working age population), and thus affects the balance between those who pay taxes and those who
consume taxes (education in this case for example).
Table 1 finally shows the total fertility rates in each of the EU-27 countries: last observed and in 2030
according to the friendly, the tough and the EUROPOP2010 scenarios
In both friendly and tough scenarios the age specific fertility rates used in the EUROPOP2010 population
projections (Eurostat) are basis in the projections. These age specific rates are adjusted to reach the
scenario specific TFRs.
15
Table 1 Total fertility rates observed and projected by 2030 according to different scenarios
Country
Austria
Belgium
Bulgaria
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
5.2
Last observed
2009 or 2010
1.44
1.84
1.49
1.51
1.49
1.87
1.63
1.87
2.03
1.39
1.51
1.25
2.07
1.41
1.17
1.55
1.63
1.38
1.79
1.38
1.36
1.38
1.40
1.57
1.38
1.98
1.94
NEUJOBS friendly
2030
1.72
1.92
1.75
1.76
1.75
1.94
1.82
1.94
2.02
1.70
1.76
1.63
2.04
1.71
1.59
1.78
1.82
1.69
1.90
1.69
1.68
1.69
1.70
1.79
1.69
2.00
1.97
NEUJOBS tough
2030
1.44
1.84
1.49
1.51
1.49
1.87
1.63
1.87
2.03
1.39
1.51
1.25
2.07
1.41
1.17
1.55
1.63
1.38
1.79
1.38
1.36
1.38
1.40
1.57
1.38
1.98
1.94
EUROPOP2010
2030
1.46
1.84
1.60
1.55
1.55
1.84
1.66
1.86
1.98
1.43
1.57
1.40
2.04
1.48
1.39
1.59
1.63
1.50
1.80
1.46
1.40
1.45
1.48
1.58
1.46
1.92
1.93
Life expectancy
Past life expectancies
Life expectancy has been increasing in most developed countries worldwide over very long time periods.
Since 1960, there have been significant increases in life expectancy at birth in all Member States. Between
1960 and 2009, life expectancy at birth has increased significantly, especially for women. In the EU, the gap
between female and male life expectancy has diminished since 1990, due to faster improvements in life
expectancy for males relative to females. Since 2000, the increase in life expectancy has been 2.2 for
females and 2.6 for males (EUROPOP2010).
The gains in life expectancy at birth have differed across countries between 1960 and 2009. Women have
gained 11 years or more in Germany, Spain, France, Italy, Luxembourg, Malta, Portugal and Finland. Smaller
increases of 8 years or less were observed in Bulgaria, the Czech Republic, Denmark, Latvia and Slovakia.
Gains in the life expectancy over the same period for men have been 11 years or more in Germany, Spain,
France, Italy, Luxembourg, Malta, Austria, Portugal and Finland, while increases of 7 years or less have
occurred in Bulgaria, the Czech Republic, Denmark, Estonia, Latvia, Lithuania, Hungary, Poland and Slovakia
16
(EUROPOP2010). In 2009, life expectancy at birth for females ranged from 77.4 in Romania and Bulgaria to
85 years in France, and for males ranging from 67.5 in Lithuania to over 79.4 in Sweden (EUROPOP2010).
EUROPOP2010 life expectancies
There is no consensus among demographers on trends over the very long term, e.g. whether there is a
natural biological limit to longevity, the impact of future medical breakthroughs, long-term impact of public
health programmes and societal behaviour such as reduction of smoking rates or increased prevalence of
obesity. Past population projections from official sources have, however, generally underestimated the
gains in life expectancy at birth as it was difficult to imagine that the reduction of mortality would continue
at the same pace in the long run (EUROPOP2010). In the EU, life expectancy at birth for males is projected
to increase by 7.9 years over the projection period, from 76.7 in 2010 to 84.6 in 2060. For females, life
expectancy at birth is projected to increase by 6.5 years, from 82.5 in 2010 to 89.1 in 2060, implying a
convergence of life expectancy between males and females. The largest increase in life expectancy at birth,
for both males and females, are projected to take place in the Member States with the lowest life
expectancy in 2010. Given the assumed ‘convergence hypothesis’, the projection compresses the spread of
life expectancy at birth for both males and females across the Member States by the end of the projection
period (EUROPOP2010).
NEUJOBS life expectancies
Considering observed trends in the past, learning from neighbours, and sharing knowledge it seems not
very likely that life expectancies will decline in the future, irrespective of the scenario. We can therefore
only assume different trajectories in the increase in life expectancy. French and Italian women nowadays
have the highest life expectancies at birth in the EU. Looking at the trend in these life expectancies in the
last 25 years you may easily see a linear trend (figure 4).
Figure 4
Base models for female life expectancies
90
88
86
84
82
80
78
1986
1991
2001
1996
2006
average of French and Italian women
e0 of Italian women
model with 1.5625% decline in growth
2011
2016
2021
e0 of French women
linear model
2026
The linear model on the average life expectancies of French and Italian women has the form:
y = 0.2166x + 79.592.
This means that each calendar year the average life expectancy at birth of French and Italian women
increased with 0.22 years.
17
In our friendly scenario we assume that life expectancies of women will converge to this linear trend. But,
as was said earlier not all countries converge equally fast. A simple test comparing (observed) differences in
2000 and 2010 (for all countries) to the linear model, reveals that especially the East-European countries 3
converge relatively fast, whereas the West-European countries 4 converge too, but at a slower pace. In the
period 2000-2010 the average difference for Eastern-European countries to the linear model declined with
a factor of 0.7566 (the difference decreased with almost 25 per cent (= 1 – 0.7566)), and in WesternEuropean countries with a factor of 0.9249 5 (the difference decreased with 7.5 per cent (= 1 – 0.9249)). In
order to translate these (ten year based) factors to our projection period of 20 years we will use a factor of
0.5133 for East-European countries, and 0.8497 for West-European countries (double the difference from
one).
So, over a period of twenty years, the life expectancies at birth of women will follow the linear model for
average FR and IT women in such a way that observed differences will become smaller: for West-European
countries with 15% and for East-European countries with almost 50%.
In the friendly scenario we assume for men that their life expectancies will basically follow the same
trajectories as the life expectancies of women, taking into account that current observed (country-specific)
differences between life expectancies for men and women will become smaller. In EUROPOP2010 the
average decline in the difference equals ten per cent in the period 2010-2030. In the friendly scenario we
assume the difference in life expectancies between men and women become even smaller: by 2030 the
difference will be 75% of what it is now. Life expectancies for men will thus grow faster than those for
women.
In the tough scenario similar assumptions are made, with the exception of the model on FR and IT women.
It is now assumed that the increase will not be linear, but will decrease with a specific percentage per year
as compared to the original linear function. The percentage decline should be chosen in such a way that the
growth in life expectancy has stopped (i.e. a growth of zero) by the end of the projection period (2030).
Calculation shows that the percentage decline per year should equal 1.5625 (see figure 4). Convergence
factors for West- and East-European countries are the same as in the friendly scenario. For the sex
difference we assume that by 2030 it is 90% of what it is now, as was assumed in EUROPOP2010.
As an example, figure 5 shows for men and women in Germany how life expectancies will behave, following
both the friendly and tough scenarios. Tables 2 (men) and 3 (women) finally show the life expectancies for
all EU countries as they are currently being observed, and projected in 2030 by EUROPOP2010 and the
NEUJOBS scenarios. In both friendly and tough scenarios the age specific mortality rates used in the
EUROPOP2010 population projections (Eurostat) are basis in the projections. These age specific rates are
adjusted to reach the scenario specific life expectancies.
3
East: BG, CZ, EE, CY, LT, LV, HU, MT, RO, SK, SI.
West: BE, DK, DE, IE, GR, ES, FR, IT, LU, NL, AT, PL, PT, FI, SE, UK.
5
Since countries with high life expectancies, show little convergence to the model, their factors disrupted the picture
and were therefore excluded from the calculation (IT, ES, FR, SE). These four countries are the countries with the
highest observed life expectancies in both 2000 and 2010 in the EU.
4
18
Figure 5
Life expectancies in Germany
90
88
86
84
82
80
78
76
74
72
70
1985
1990
1995
2000
2005
2010
2015
2020
2025
Males friendly scenario
Females friendly scenario
Males tough scenario
Females tough scenario
2030
Observed 1985-2010, projected thereafter.
Table 2 Life expectancy at birth for men observed and projected by 2030 according to different
scenarios
Country
Austria
Belgium
Bulgaria
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
Last observed
2009 or 2010
77.9
77.3
70.3
78.6
74.5
77.2
70.6
76.9
78.3
78.0
78.4
70.7
78.7
79.4
68.6
68.0
77.9
79.2
78.9
72.1
76.7
69.8
71.7
76.4
79.1
79.6
78.3
NEUJOBS friendly
2030
83.9
83.5
80.1
85.0
82.4
83.1
79.5
83.1
84.3
83.9
84.2
80.1
84.4
85.3
78.6
78.0
83.9
85.3
84.6
79.2
82.9
79.8
80.7
83.3
84.9
85.1
84.2
19
NEUJOBS tough
2030
80.0
79.7
76.0
81.2
78.4
79.4
75.0
79.1
80.2
80.1
80.5
75.9
80.7
81.5
74.1
73.3
80.0
81.6
80.9
74.9
78.9
75.7
76.5
79.3
81.0
81.5
80.6
EUROPOP2010
2030
80.7
80.5
75.4
81.3
78.2
80.2
75.0
80.0
81.1
80.8
80.9
75.5
80.3
81.8
74.0
73.5
80.9
80.8
81.5
76.4
79.9
75.3
76.2
79.4
81.6
82.1
81.4
Table 3 Life expectancy at birth for women observed and projected by 2030 according to different
scenarios
Country
Austria
Belgium
Bulgaria
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
5.3
Last observed
2009 or 2010
83.5
82.8
77.4
83.6
80.9
81.4
80.8
83.5
85.3
83.0
82.8
78.6
83.2
84.6
78.4
78.9
83.5
83.6
83.0
80.7
82.8
77.4
79.3
83.1
85.3
83.6
82.5
NEUJOBS friendly
2030
88.1
87.6
85.4
88.7
87.2
86.3
87.2
88.1
89.6
87.6
87.5
86.1
87.8
89.2
85.9
86.2
88.1
88.6
87.6
85.7
87.5
85.5
86.4
88.4
89.6
88.1
87.4
NEUJOBS tough
2030
85.0
84.6
82.4
85.7
84.2
83.2
84.1
85.0
86.5
84.6
84.4
83.0
84.8
86.1
82.9
83.2
85.0
85.6
84.6
82.6
84.4
82.5
83.4
85.3
86.5
85.1
84.3
EUROPOP2010
2030
85.6
85.4
81.5
85.4
83.6
84.3
83.6
85.9
87.0
85.4
85.1
82.4
85.0
86.6
82.1
82.4
85.8
85.3
85.5
83.5
85.1
81.6
82.7
85.1
86.9
86.0
85.4
International migration
Past migration
European countries have gradually become a destination for migrants, starting in the 1950s in countries
with post-war labour recruitment needs and with colonial past. Southern European countries became net
receiving countries during the 1990s and several countries in Central and Eastern Europe are currently both
source and destination of migrants. Three distinct phases of immigration can be identified in the last half
century (EUROPOP2010):
-
the guest worker phase, with programmes to recruit foreign workers to cope with increasing labour
demand during the economic boom in the 1950s and 1960s in Austria, Denmark, Germany,
Luxembourg, Belgium, France, the Netherlands and the UK. They turned to other European countries,
such as Italy, Portugal and Spain, and/or to former colonies or neighbouring countries: North Africa
in the case of France and Belgium; the Caribbean and the Indian subcontinent for the UK; and
Yugoslavia and Turkey for Germany. Foreign labour recruitment stopped in 1974, after the first oil
price shock and subsequent rise in unemployment;
20
-
-
immigration continued, mostly due to family reunification: net migration flows during the 1970s
were of 240,000 people per year on average as immigrants who were present in these countries
decided to stay and were joined by their families from their home countries;
the asylum seekers phase: after a brief period of net outflows during the early 1980s recession, net
migration flows rose again, peaking in 1991-1992, as the fall of the “iron curtain” and a number of
wars and ethnic conflicts, such as in former Yugoslavia, pushed upwards the number of people
seeking asylum (EUROPOP2010).
Net inflows dropped significantly between 1992 and 1997, partly due to tighter controls over migratory
flows in the main receiving countries, but they resumed their growth at the end of the 1990s. Overall, the
average annual net entries for the EU25 more than tripled from around 198,000 people per year during the
1980s to around 750,000 people per year during the 1990s. High clandestine migration also marks the
decade of the 1990s. In the beginning of the 2000s the net migration flows to the EU27 countries
encountered a vigorous increase, totalling more than 2,000,000 in 2003 (EUROPOP2010).
Net migration flows per country are characterized by high variability. Traditionally, Germany, France and
the UK record the largest number of arrivals in the EU, but in the last decade there has been a rise of
migration flows to Italy, Spain and Ireland that have switched from countries of origin to destination
countries. After high migration inflows to the EU in the first half of the 2000s, flows were reduced
drastically and even turned into outflows in some countries that previously had experienced sharp
increases (EUROPOP2010).
EUROPOP2010 migration
Migration flows are assumed to subside in the very long-term. The basic assumption on migration is that
immigration and emigration flows tend to converge towards a common level, which is different for each
country and dependent on the latest observed values. Additional immigration flows are assumed to take
place if the projected age structure of the countries’ populations reveals a shrinking number of persons in
the working age population. The theoretical common point for the two flows is not assumed to be reached
within the time horizon of the projections (EUROPOP2010).
For the EU as a whole, annual net inflows are projected to increase from about 1,018,000 people in 2010
(equivalent to 0.2% of the natural EU population) to 1,217,000 by 2020 and thereafter declining to 878,000
people by 2060. The cumulated net migration to the EU over the entire projection period is 55 millions, of
which the bulk is in the euro area (42 millions). Net migration flows are projected to be concentrated to a
few destination countries: Italy, Spain and the UK. According to the assumptions, the change of Spain and
Italy from origin countries of migration in the past to destination countries in the future will be confirmed
in the coming decades. For countries that are currently experiencing a net outflow , this is projected to
taper off or even reverse in the coming decades (EUROPOP2010).
NEUJOBS migration
Migration is the most difficult component to make assumptions of. All countries have different histories
and each has its own specificities. The net migration numbers from EUROPOP2010 account for both these
characteristics, and in addition they take into account that a declining potential labour force population
may attract additional migrants. Rather than developing new migration assumptions, we use the
EUROPOP2010 assumptions as our point of reference.
21
For migration we assume bandwidths around the EUROPOP2010 net migration assumptions. Rather than
taking the yearly net migration numbers, we took the 2010 and 2030 net migration numbers of
EUROPOP2010 and assume a linear trend between the two. Based on observed values over the period
1990-2010, the standard deviation for each country is calculated. For our friendly scenario we assume more
net migration than in EUROPOP2010. In 2010 net migration is the same as in EUROPOP2010, by 2020 half
of the standard deviation is added to EUROPOP2010, and by 2030 one standard deviation is added to the
EUROPOP2010 value for 2030. Higher migration numbers lead to larger working age populations, and thus
to lower dependency ratios and higher GDP growth rates. For the tough scenario we reason the other way
around. In the tough scenario pressure on the environment is higher, caused by lower net migration flows.
In this case the standard deviation is subtracted from the EUROPOP2010 values. Figure 6 shows how this
turns out for Germany.
Figure 6
Net migration numbers for Germany (x 1,000)
800
600
400
200
0
-200
1990
1995
2000
observed
2005
2010
2015
2020
2025
2030
f riendly
tough
Observed between 1990 and 2010, thereafter according to the tough and friendly NEUJOBS scenarios (2010-2030).
Table 4 shows the net migration numbers for all EU countries as they are currently being observed, and
projected in 2030 by EUROPOP2010 and the NEUJOBS scenarios.
In both friendly and tough scenarios the age specific net migration numbers used in the EUROPOP2010
population projections (Eurostat) are basis in the projections. These age profiles are adjusted to reach the
scenario specific net migration numbers.
22
Table 4 Net migration numbers observed and projected by 2030 according to different scenarios
(x 1,000)
Country
Austria
Belgium
Bulgaria
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Luxembourg
Malta
Netherlands
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
United Kingdom
5.4
Last observed
2009
21.1
64.1
-15.7
1.8
28.3
15.3
0.0
14.6
70.0
-10.7
35.1
17.3
-27.6
311.6
-4.7
-15.5
6.6
-0.2
38.5
-1.2
15.4
-1.6
4.4
11.5
50.3
62.6
201.3
NEUJOBS friendly
2030
58.4
62.0
50.8
9.2
56.7
18.0
11.2
13.9
169.0
366.2
60.3
25.9
45.4
549.1
13.5
7.4
4.7
2.4
37.0
91.7
64.4
150.6
18.9
11.9
513.8
43.0
255.5
NEUJOBS tough
2030
12.9
23.3
-57.4
1.8
-5.6
5.9
-11.8
5.6
5.0
-100.3
11.2
18.4
-3.9
128.2
-12.6
-9.5
2.1
-1.7
-13.4
-85.3
10.1
-144.2
-2.6
-0.6
-5.8
9.0
100.8
EUROPOP2010
2030
35.6
42.6
-3.3
5.5
25.6
12.0
-0.3
9.7
87.0
133.0
35.8
22.1
20.8
338.7
0.4
-1.0
3.4
0.4
11.8
3.2
37.2
3.2
8.2
5.7
254.0
26.0
178.1
Urbanisation
The above assumptions determine population size and age structure at the national level. At the regional
level we will make assumptions about the distribution of the population across urban and rural regions.
This distribution depends on the size of urban-rural migration within countries (internal migration), the
choice of place of residence by immigrants from abroad (international migration) and differences in the
numbers of births and deaths between urban and rural regions (natural increase). Van der Gaag et al. (2012)
describe developments in rural-urban migration during the last two decades. Rural-urban migration
depends on employment opportunities and residential preferences. Since we may assume that in the tough
scenario job opportunities will be scarce in both urban and rural regions, there is no reason to assume that
the urban-rural migration pattern will be different between the tough and friendly scenario. To determine
the regional distribution of migration we make a distinction between net internal and net international
migration. We use data for the period 2005-2009. For internal migration by NUTS 2 region and age, we use
the estimates provided by the DEMIFER project (ESPON & NIDI, 2010). Using these internal migration
estimates and age-specific data on regional net migration from Eurostat, we estimated age-specific regional
net international migration patterns. The resulting age profiles are adjusted to reach the age-specific
23
national net migration numbers. For the projection period we assume that the distribution patterns from
the past will persist. In other words, in both scenarios we assume that young people move from rural
regions to urban regions and that at older ages people move from urban to intermediate or rural regions.
As in most countries international immigrants tend to move to urban rather than rural regions, changes in
international migration will affect the regional distribution of the population. Furthermore, as residential
preferences vary with age, regional differences in the age composition of the population may also
contribute to changes in the regional distribution. Due to changing levels of international migration and
different regional population age structures at the start of the projection period, therefore, the age-specific
distribution of the population across rural and urban NUTS 2 regions will change over time. Overall this
results in slightly more urbanisation, with only very small differences between the two scenarios.
Figure 7 compares the percentage of the population living in urban regions in Germany in the friendly and
tough scenarios with the percentages in 1990 and 2010. Compared to 2010 the overall percentage of the
population in urban regions in Germany increased from almost 51 per cent to slightly over 53 per cent. The
difference between the tough (53.1 per cent) and friendly scenario (53.0 per cent) is negligible. In absolute
terms, however, the urban population in the friendly scenario outnumbers that of the tough scenario with
more than two million people.
Figure 7
Percentage of the population living in an urban region, Germany, 1990, 2010 and 2030
%
60
55
50
45
0-4
5-9
10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84
1990
5.5
2010
2030 tough
85+
2030 friendly
Level of educational attainment
The population by age and sex will be distinguished by level of educational attainment. We distinguish
three levels of educational attainment. Since age groups 0-4, 5-9, and 10-14 are still at school, for these
groups the level of educational attainment is not applicable. For this reason we start the age schedules at
age 15. Figures 8 and 9 show the level of educational attainment for men and women in Germany. The data
are obtained from the Labour Force Survey. The figures show that among young adults the share of highly
educated women is close to that of men, but at middle ages and even more so at older ages the share of
highly educated among men is considerably higher than among women.
To a large extent these differences between men and women can be attributed to cohort effects. Among
older cohorts a higher share of men than women participated in higher education. But among young
generations these differences have disappeared.
24
Figure 8
Men by level of educational attainment, Germany, 2010
Source: Labour Force Survey.
Figure 9
Women by level of educational attainment, Germany, 2010
Source: Labour Force Survey.
The future percentages of highly educated women will depend on two effects. First, if the number of young
women following tertiary education, the percentage of women with high education will increase among
young women. Secondly, a cohort effect will lead to an increase in the level of educational attainment
among middle aged women, since the middle aged women of the future are the young women of today
and they have a higher level of educational attainment than previous generations. In addition the
distribution of the population among levels of education will depend on international migration. If
European countries succeed in attracting highly skilled immigrants, this will have a positive effect on the
level of educational attainment of the population.
For the level of educational attainment we will assume an increase in the friendly scenario and a stagnation
in the tough scenario. The reason is that we assume that a stagnation of the educational level will have a
downward effect on labour productivity and an upward effect on the costs of health care. Thus a relatively
25
low level of educational attainment of the population will be more challenging for policy makers than an
expansion of high levels of education among the population.
IIASA produced projections of the population by level of educational attainment for 120 countries for the
next decades (KC, 2010). We will use two scenarios produced by IIASA. First, the Constant enrolment
scenario (CER) assumes constant proportions within each cohort. This is a worst case scenario. It takes into
account cohort effects but does not assume a further future expansion in education. We will assume that
this scenario will occur in the tough NEUJOBS scenario. Second, the Fast Track scenario is an optimistic
scenario. This scenario assumes an expansion of young generations following tertiary education. We will
assume that this scenario will occur in the friendly NEUJOBS scenario. The three categories of education
distinguished by IIASA do not match exactly with those in the LFS data set. Therefore we used the IIASA
scenarios as a guidance, rather than the exact numbers, in order to be able to use them with the LFS data.
Figures 10 an 11 compare the Fast Track scenario from IIASA in the year 2030 with the pattern for 2010.
Figure 10 Men by level of educational attainment, Germany, 2010 and 2030, Fast Track scenario
%
100
80
60
40
20
0
15-19
20-24
25-29
2010-low
30-34
35-39
40-44
2010-medium
45-49
50-54
2010-high
55-59
60-64
2030-low
65-69
70-74
75-79
2030-medium
80-84
85-89
2030-high
Figure 11 Women by level of educational attainment, Germany, 2010 and 2030, Fast Track scenario
%
100
80
60
40
20
0
15-19
20-24
25-29
2010-low
30-34
35-39
40-44
2010-medium
45-49
50-54
2010-high
55-59
60-64
2030-low
65-69
70-74
75-79
2030-medium
Fast Track is the basis for the Friendly scenario.
26
80-84
85-89
2030-high
The figures show that the percentage of men and women with a high level of educational attainment will
increase, and the share with a medium educational level will decrease. The percentage of people with low
educational levels will hardly change. According to this scenario the differences in level of educational
attainment between men and women will decrease. This is caused by the cohort effect: among young
generations the differences between men and women are smaller than among older generations. As these
young generations grow older, the differences at middle ages will decrease.
When preparing the method of education scenario setting for NEUJOBS, we assumed (similarly as observed
in the IIASA scenarios) that for the older age groups the cohort effect is dominant: the distribution by
education level in a given age group is the same as five year earlier in the age group five year younger. In
other words we assume that older people do not change their education status and that education-related
differences in mortality do not impact the distribution by education level in a significant way (or this impact
is smaller than the uncertainty of the initial estimations). We assumed that the cohort effect determines
the distribution by education level for age groups 45-49 and older in the Friendly scenario and for age
groups starting from 35-39 (or even 30-34, depending on the 2010 pattern) in the Tough scenario. So for
example in age group 75-79 the distribution by education is the same as in the age group 70-74 five years
earlier.
For the young age groups, we assumed an increase of the level of education attainment in the Friendly
scenario and no improvement (stagnation) in the Tough scenario. In the Tough scenario the shares of Low,
Medium and High in each 5-year age group (below 30 or 35) and sex are the same as estimated from the
LFS 2010 data. In the Friendly scenario we assumed that the increase of the share of the High education
category in the age groups 20-44 during the period 2010-2030 will be the same (in absolute terms) as the
increase in the corresponding age groups according to the IIASA Fast Track scenario. Additionally, we
assumed that the share of persons with higher education cannot be lower than if estimated based on the
cohort effect. For the 15-19 age group, the percentage of persons with higher education was projected by
IIASA to be always zero, but is non-zero in some countries according to the LFS data. For this age group, we
assumed that the percentage increase of the share of High in the Friendly scenario will be the same as an
average percentage increase for the 20-44 age group in the IIASA Fast track scenario.
Scenarios for the share of persons with Low and Medium level of education attainment in the Friendly
scenario were based on the assumptions that between 2010 and 2030 the share of persons with Medium
level of education among persons who did not complete higher education will increase by 6 per cent (with
the restriction that it cannot be higher than 97 per cent in 2030). IIASA's projections do not distinguish
between the Low and Medium level, so we had to make our own assumptions here. The shares for 2015,
2020 and 2025 were calculated assuming a linear increase between 2010 and 2030.
5.6
Household position
The population by age, sex and level of educational attainment will be distinguished by household position.
We will distinguish six household positions.
1.
2.
3.
4.
Living at parental home.
Living alone.
Living with a partner without children.
Living with a partner with children.
27
5.
6.
Lone parent.
Other.
Iacovou and Skew (2011) distinguish four types of household structure in Europe. In the ‘Nordic’ cluster
including Sweden, Denmark, Finland and the Netherlands, there are relatively many one-person
households and lone-parent households. Many young people live alone before they start cohabitation or
marriage. Relatively many elderly women live alone when they have become widow. In the ‘Southern’
cluster including Italy, Spain, Portugal and Greece, there is extended co-residence of parents and their adult
children and of elderly people with their adult off-spring. As a result the share of people living alone is low.
In addition there are less lone-parent families. The ‘North-Western’ cluster includes the UK, France,
Germany, Belgium, Luxembourg and Ireland, bit also Czech Republic and Hungary, have an intermediate
position between the other two groups. The ‘Eastern’ cluster including Bulgaria, Romania, Slovakia, Poland
and Slovenia, has relatively large households. There is a virtual absence of solo living among young people.
There is extended inter-generational co-residence and a relatively low share of lone-parent families. The
Baltic states have a separate pattern, as they combine high levels of extended families with a very high rate
of lone parenthood.
Figures 12 and 13 show the age pattern of the percentage of men and women living alone in Germany
distinguished by level of educational attainment. We show high and low levels of educational attainment
only, as the share of single people among those with a medium level of educational attainment lies
between that of low and high education levels. The figures show that among young highly educated
women the percentage of singles is considerably higher than among women with a low education. The
explanation is that highly educated people tend to postpone living together with a partner. At older ages
the percentage of women living alone is considerably higher than that of men. The explanation is that
women live longer than men. Moreover, most women tend to marry with a man who is a couple of years
older. As a consequence there are more widows than widowers.
Figures 14 and 15 show that the percentage of young men and women living together with a partner but
who do not (yet) have a child is higher among highly educated persons than among people with a lower
education. This is explained by the fact that highly educated couples tend to postpone the birth of their first
child. At middle ages the percentage of women with low education who live together with a partner but
who do not have a child in the household is high as a result of the fact that their children have left the
parental home. Since highly educated women have their children some years later, they are older when
they enter the ‘empty nest’ stage. At older ages the percentage of men living with a partner is higher than
among women since men live together with a woman who is a couple of years longer. As a result relatively
few men are widower.
Figures 16 and 17 show that the percentage of young women with a low educational level living with a
partner and with children is higher than among women with a high level of educational attainment as they
have children at a younger age. At middle ages the percentage among women with low education is lower
as children have left the parental home.
28
Figure 12 Men living alone by level of educational attainment, Germany, 2010
Figure 13 Women living alone by level of educational attainment, Germany, 2010
Figure 14 Men living with partner without children by level of educational attainment, Germany, 2010
29
Figure 15 Women living with partner without children by level of educational attainment, Germany,
2010
Figure 16 Men living with partner with children by level of educational attainment, Germany, 2010
Figure 17 Women living with partner with children by level of educational attainment, Germany, 2010
30
The percentage of single parents among men is very low (figures 18 and 19). If couples divorce, only in few
cases the children will live with the father. The relatively high share of single mothers at young age among
women with a low education can be explained by the fact that women with low education tend to marry at
a younger age, and a relatively large share of these young marriages lead to a divorce.
Figure 18 Men living without partner with children by level of educational attainment, Germany, 2010
Figure 19 Women living without partner with children by level of educational attainment, Germany,
2010
The distribution of the population over household positions will change as a consequence of changes in the
distribution of the population over levels of educational attainment. The joint distribution by household
position and level of education attainment was calculated as a product of the forecasted distribution by
education level and the distribution by household position as estimated from the 2010 LFS data. Since the
percentage of young singles among highly educated is relatively high, an increase in the percentage of
people with a high level of educational attainment, will lead to an increase in the percentage of singles.
Since in the friendly scenario the percentage of highly educated people is higher than in the tough scenario,
the distribution of the population by household position in the friendly scenario will be closer to that of
highly educated people than in the tough scenario.
31
6
Results for Germany
In order to illustrate the demographic scenarios we show results for Germany. The total population in
Germany (figure 20) decreases in both tough and friendly scenarios. In the friendly scenario the population
decreases with 1.0 million people (1.2%) to a total of 80.83 million people and in the tough scenario with
9.5 (11.6%) to a total of 72.32 million people. The decrease in the tough scenario is stronger than in the
friendly scenario. The working age population (figure 21) decreases even faster: with 12.0 % in the friendly
scenario and 18.3 % in the tough scenario. This is mainly due to differences in assumptions on migration
between the two scenarios.
Figure 20 Total population according to different scenarios, Germany, 2010-2030 (millions)
84
82
80
78
76
74
72
2010
2015
2020
friendly
2025
2030
tough
Figure 21 Total working age population according to different scenarios, Germany, 2010-2030 (millions)
54
52
50
48
46
44
2010
2015
2020
friendly
2025
tough
32
2030
Figure 22 shows the age structure of the German population in 2030. The largest differences are visible for
the ages below 50-54. These are the age group affected most by migration flows. The higher assumed life
expectancies in the friendly scenario are especially visible at age group 85+. Note that the effect of the
higher fertility rates in the friendly scenario are reinforced by the high number of immigrants in the friendly
scenario: on average women have more children in the friendly scenario, and there are more women to
bear children.
Figure 22 Age structure in 2030 according to different scenarios, Germany, 2010-2030 (millions)
8
6
4
2
0
0-4
5-9
10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84
friendly
85+
tough
In figure 23 the old age depend ratios are plotted. The old age dependency ratio is calculated as the
number of 65+ year olds divided by the working age population (15-64 year old). The measure is also
known as grey pressure.
Figure 23 Old age dependency ratio according to different scenarios, Germany, 2010-2030
0,55
0,50
0,45
0,40
0,35
0,30
0,25
2010
2015
2020
friendly
2025
2030
tough
In both scenarios the ratio increases quite substantially from 0.314 in 2010 to 0.461 in the friendly scenario
and to 0.447 in the tough scenario in the year 2030. This is an increase of 47 and 42 per cent respectively.
Figure 24 shows a similar figure, the total dependency ratio. It now also includes the youngest age groups.
This measure is also known as demographic pressure. It is calculated as the total of the number of 0-14 year
olds and the number of 65+ year olds divided by the working age population. The increases in the ratio are
now even a bit larger: from 0.52 to 0.69 in the friendly and to 0.65 in the tough scenario.
33
Figure 24 Total dependency ratio according to different scenarios, Germany, 2010-2030
0,75
0,70
0,65
0,60
0,55
0,50
2010
2015
2020
friendly
2025
2030
tough
Figures 25-27 show the age and sex structure of the German population in 2010 (figure 25) and in 2030 for
the friendly (figure 26) and tough scenario (figure 27). Since each person grows one year older every
calendar year, the basic shape of the pyramid has shifted upwards (20 years (2030 minus 2010)). Between
figure 26 and figure 27 the differences become apparent at the oldest age groups (differences in life
expectancies) and in age groups 15-19 to 35-39 (differences in assumptions on international migration).
Please note the difference in length of the bars for men and women at the highest ages: women have
higher life expectancies than men, and the differences between the sexes become smaller in the friendly
scenario than in the tough scenario.
Figures 28-36 show similar results, but now for the three different types of region (rural, intermediate and
urban respectively). The general result for the three regions is that the negative effects are the strongest
for the predominantly rural region and the weakest for the predominantly urban region. The total
population in the predominantly rural region decreases with 9.7% in the friendly and 19.7% in the tough
scenario between 2010 and 2030. For the intermediate region these figures are 2.9% and 13.2%
respectively and for the urban region 7.5% in the tough scenario. The friendly scenario for urban regions is
the only scenario that results in a growth of the population: the population in urban regions grows with 3.1%
in the period 2010-2030. The percentage of 65+ in the predominantly rural region increases from 21.2% in
2010 to 31.1% (tough) and to 31.0% in the friendly scenario in 2030. For the intermediate region these
figures are 20.9%, 28.6% and 28.7% respectively and for the predominantly urban region 20.3%, 24.9% and
25.3%. In 2010 the total dependency ratio in 2010 is highest in the intermediate region (0.53) and increases
to 0.73 in the friendly scenario and to 0.69 in the tough scenario in 2030. For the predominantly urban
region these figures increase from 0.51 in 2010 to 0.64 (friendly) and 0.60 (tough) in 2030. For the
predominantly rural region the figures are the highest: from 0.52 in 2010 to 0.76 (friendly) and 0.73 (tough)
in 2030.
Figures 37-39 show again the sex and age structure for the German population, but now also distinguished
by education level. The assumed effects of more medium educated in the tough scenario and more highly
educated people in the friendly scenario are nicely visible.
34
Figure 25 Population pyramid Germany, 2010 (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 26 Population pyramid Germany, 2030, friendly scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 27 Population pyramid Germany, 2030, tough scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
Left: men, right: women.
35
4
5
Figure 28 Population pyramid, predominantly rural regions, Germany, 2010 (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 29 Population pyramid, predominantly rural regions, Germany, 2030, friendly scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 30 Population pyramid, predominantly rural regions, Germany, 2030, tough scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
Left: men, right: women.
36
4
5
Figure 31 Population pyramid, intermediate regions, Germany, 2010 (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 32 Population pyramid, intermediate regions, Germany, 2030, friendly scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 33 Population pyramid, intermediate regions, Germany, 2030, tough scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
Left: men, right: women.
37
4
5
Figure 34 Population pyramid, predominantly urban regions, Germany, 2010 (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
2
1
3
4
5
Figure 35 Population pyramid, predominantly urban regions, Germany, 2030, friendly scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
4
5
Figure 36 Population pyramid, predominantly urban regions, Germany, 2030, tough scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
1
0
1
2
3
Left: men, right: women.
38
4
5
Figure 37 Population pyramid by educational level, Germany, 2010 (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
n.a.
1
0
low
1
2
medium
3
4
5
high
Figure 38 Population pyramid by educational level, Germany, 2030, friendly scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
n.a.
1
0
low
1
medium
3
2
4
5
high
Figure 39 Population pyramid by educational level, Germany, 2030, tough scenario (%)
85+
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-14
5-9
0-4
5
4
3
2
n.a.
1
low
0
1
medium
2
3
high
Left: men, right: women.
39
4
5
7
References
ESPON & NIDI (2010), DEMIFER Demographic and Migratory Flows affecting European Regions and Cities.
Final Report.
European Commission (2011), The 2012 Ageing report: Underlying assumptions and projection
methodologies. Joint report prepared by the European Commission (DG ECFIN) and the Economic
Policy Committee(AWG).
Giannakouris, K. (2010), Regional population projections EUROPOP2008: Most EU regions face older
population profile in 2030. Statistics in Focus 1/2010.
Iacovou, M. and A.J. Skew (2011), Household composition across the new Europe: Where do the new
Member States fit in? Demographic Research 25, 465-490.
KC, S., Barakat, B., Goujon, A., Skirbekk, V., Sanderson, W. and Lutz, W. (2010), Projection of populations by
level of educational attainment, age, and sex for 120 countries for 2005-2050. Demographic
Research 22, 383-472.
Van der Gaag, N., J. de Beer, R. van der Erf and C. Huisman (2012), Migration, urbanization and
competitiveness: What regions are most vulnerable to the consequences of a declining working age
population? NEUJOBS Working Paper 8.2
40