DEPARTMENT OF FAMILY AND COMMUNITY SERVICES
POLICY RESEARCH PAPER NO. 13
The policy-maker’s guide to
population ageing: key concepts
and issues
Natalie Jackson
School of Sociology and Social Work, University of Tasmania
© Commonwealth of Australia 2001
ISSN 1442–7532
This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced
by any process without prior written permission from the Commonwealth, available from AusInfo. Requests and
inquiries concerning reproduction and rights should be addressed to the Manager, Legislative Services, AusInfo,
GPO Box 1920, Canberra ACT 2601 or by email to cwealthcopyright@dofa.gov.au
Acknowledgements
An earlier and shorter version of this work appears in Australian Social Policy, 1/1999, pp. 203–224. This version
updates that material.
The work is currently being translated into Mandarin. It will appear under the title of ‘Ren Kou Lao Nian Hua Gong
Ju Xian’, in She Hui Bao Zhang Can Kao Da Quan [Social Security Reference Manual], Department of Family and
Community Services /World Bank China Enterprise Housing and Social Security Reform Project (CD-ROM, in press).
The views expressed in this paper are those of the author and do not represent the views of the Minister for Family
and Community Services or the Department of Family and Community Services.
June 2001
Department of Family and Community Services
PO Box 7788
Canberra Mail Centre ACT 2610
Telephone: 1300 653 227
Internet: www.facs.gov.au
ii
Contents
Executive summary
v
1
Understanding population ageing
1
2
The birth rate
5
3
Measuring life expectancy
9
4
Indices of ageing
11
5
The birth rate, cohort size, population ageing
15
6
Natural increase and decrease; doubling and halving time
21
7
Is migration the answer?
23
8
Sub-population differences
27
9
Demographic compression
33
10 Age structure and the welfare state—a ‘social’ or ‘demographic’ contract?
35
11 Policy and population ageing
39
12 Population projections
43
13 Methodological implications—some useful techniques
45
Endnotes
49
Bibliography
51
Useful web sites
55
Figures
1
The demographic transition (classic or western model)
1
2
Age-sex structure of the Australian population, 1976, 1996 and 2016
4
3
Median ages of mothers (all births), Australia 1921–98
6
4
Total fertility rate 1921–99, and completed fertility rate for cohorts born 1905–60
lagged by 30 years, Australia
7
Youth (0–19 years), aged (65+ years) and total (0–19 and 65+ years) dependency
ratios, Australia 1971–2051
12
6
Total fertility rates and cohort size, Australia, 20th Century
16
7
Projected increase in populations aged 55–64, 65–74 and 75+ years, Australia
18
8
Labour market entry-exit ratios (18–24:55–64 years), Australia 1971–2051
19
9
Births and deaths, Australia, 20th Century, and projected
22
5
10 Age-sex structures of the Aboriginal and Torres Strait Islander and total
Australian populations, 1996
27
11 Percentage of each age group born overseas, by region of birth, 1998–99
30
12 Projected percentages aged 65+ years, selected States and Territories
31
iii
The policy-maker’s guide to population ageing: key concepts and issues
13 Projected rate of natural increase and decline (per 1 000 persons), by State
and Territory
32
14 Projected changes in numbers of females receiving Age Pension under
different eligibility criteria
36
15 Components of change in disability support pension (percentage point change
over 1971), males 1971–97
47
Tables
1
Age-specific and total fertility rates, Australia 1986 and 1998
5
2
Life expectancy at birth, and on reaching that age, Australian cohorts born 1932
(aged 68 years in 2000)
9
3
Median ages and projections of aged for selected countries and regions,
2001 and 2020
11
4
Projected aged/child and parent support ratios, Australia 2000–50
13
5
Current and projected size, and annual net number of migrants to achieve
scenario outcomes, by selected country or region and scenario
23
Median age and percentage of Australian population aged 65 years and over,
by birthplace, 1981, 1991 and 1999
29
6
iv
Executive summary
Executive summary
Over the past two and a half centuries, the developed world has experienced what is arguably
one of humanity’s greatest achievements: the demographic transition. This transition, denoted
by a fall from high to low fertility and mortality, has taken place in every developed country,
and is currently under way in every developing country. It has brought and continues to bring
with it a number of momentous changes, most notably a shift from youthful age structures and
expansive growth to ageing and stationary or declining populations.
The implications of the shifts are profound. More than any phenomenon in the recent past,
they will challenge our social, economic, political and cultural structures, and the policymaking communities that must respond to these changes. However, while the implications of
the transition are increasingly understood and recorded, a user-friendly explanation of the
basic demography on which they are based is typically missing. The gap, usually a reflection
of word constraints, causes problems for those who recognise the importance of
understanding what is going on, yet are at a loss to know where to begin.
This ‘toolkit’, a compendium of concepts and specifically related to population ageing, is
especially written for busy policy-makers, advisers and analysts. It does not purport to cover
these concepts and issues comprehensively, but rather, to outline the key principles involved,
and to indicate where further information may be found. The main points are summarised
below.
• The demographic transition has one major outcome: a shift from youthful and growing
populations to populations that are ageing and potentially declining. For policy purposes, a
distinction needs to be made between structural and numerical ageing. The former refers to
an increase in the proportion of aged in the population, and is primarily caused by falling
fertility. Assuming a continuation of low fertility, the main effect of structural ageing will be
to reduce the size of the working-age population/primary tax base in comparison with the
increasing proportion of elderly. Numerical ageing, on the other hand, refers to an absolute
increase in the number of aged, and is primarily caused by increasing life expectancy, first
at the younger ages, then at older ages.
• The total fertility rate (TFR), which is used as a proxy for average family size, is a synthetic
measure with many limitations. Most importantly, it conceals both the effect of delayed and
recuperated fertility, and the proportion of women having no children at all. Over time,
actual completed family size is typically higher than the lowest TFR, and lower than the
highest TFR.
• Life expectancy specifies the additional number of years a person in a given birth cohort
can expect to live beyond a reference age. Typically what is referred to is life expectancy at
birth. This changes over the life cycle. When considering future demand for elder-oriented
goods and services, it is important to be aware of measures of life expectancy at older ages,
for example, age 65.
v
• A population is considered young when it has a median age of less than 20 years (or less
than 5 per cent aged 65 years and over), and old when it has a median age of more than 30
years (or more than 10 per cent over the age of 65). Other useful indices of population
ageing are the aged/child ratio and the familial support ratio. The more commonly used
dependency and potential support ratios have many limitations. Uppermost among these is
that they treat the working-age population (15–64 years) as if all its members of it were
economically active.
• A cohort is a group of people connected by a similar event (for example, birth in a given
year). The size of a birth cohort is the combined function of prevailing birth (and mortality)
rates and the number of women at reproductive age (and actually giving birth). This caused
Australia’s largest birth cohort to be born in 1971, rather than 1961 (the peak of the baby
boom). It also means that most baby bust cohorts are larger than most baby boom cohorts.
Changes in cohort size should not be confused with population ageing—the large cohorts
born during the baby boom years initially made the population younger.
• A youthful age structure typically contains a momentum of population growth, while an
older age structure contains a momentum of decline. The momentum effect is the
unavoidable growth or decline potential contained with the age structure. For example, at
the same time as the number of births are declining (causing structural ageing), the
increased numbers of elderly (the result of numerical ageing) are causing an increase in the
number of deaths. The two trends are on a seemingly unavoidable collision course that, in
Australia, will see a shift from natural increase to natural decline around 2035.
• Net migration gains can have small reducing effects on structural ageing, but, in the long
term, add to both structural and numerical ageing. Attempts to maintain either the size of
the working-age population, or the ratio of working-age to elderly through replacement
migration, would increase the size of host populations beyond what is believed to be
socially or politically acceptable. (Replacement migration aimed solely at maintaining
population size (in the context of intrinsic decline) is an exception.) A useful means of
understanding the trade-off is McDonald and Kippen’s index of efficiency, which
demonstrates the percentage reduction in structural ageing for each net million migrants
gained. Fertility increase is argued to be a more efficient counter to population ageing than
immigration. However, substantial fertility increase may now be unattainable, and in the
short term would add to the total dependency ratio.
• Within the total population there are different age structures for different sub-population
groups (sex, ethnic and regional). The Aboriginal and Torres Strait Islander population is
considerably younger than the total Australian population, while the main immigrant groups
of the 1940s and 50s are considerably older. Among the latter, males tend to outnumber
females at older ages, which differs from the total population. Differences in age structure
by State and Territory indicate that Tasmania and South Australia will begin intrinsic decline
several decades before the remaining States and Territories.
vi
Executive summary
• Demographic compression occurs when a number of key demographic events (such as
age at child birth, age at which the last child leaves home, the length of the working life/
retirement, the ageing of parents), become compressed into a shorter space of the life cycle.
They may also overlap with the demographic experiences of an individual’s own parents
and offspring. Different cohorts may have different abilities to respond to inter-generational
demands.
• The welfare states of most developed countries were developed at a time when the age
structures of these countries were young and juvenescent. The ‘social contract’, pay-as-yougo type of welfare state may in fact require a more youthful demographic structure for
long-term sustainability. If so, the Australian welfare state (and others like it) may have a
built-in ‘use-by’ date.
• Policy has many dimensions, among which are explicit, implicit, direct, indirect,
unintentional, and net effects. These sometimes conflicting dimensions mean that it is
almost impossible to attribute a change in a social phenomenon to any single policy
intervention. However, ostensibly non-demographic policies (such as higher education fees)
can have demographic effects. Where possible, policies should be scrutinised for their
potential anti-natal effects.
• Population projections are not predictions. They are computed on clearly specified and
biennially revised sets of assumptions about birth, death and migration rates. Because birth
and death rates change slowly, and migration into a country such as Australia can be
reasonably well controlled and monitored, projections for the immediate years and decades
are highly reliable. It is typical to use the medium variant assumptions for regional and
international comparisons.
• When undertaking statistical analysis of social phenomena over time, it is important to
distinguish between changes due to shifts in age structure (or changes due to other
compositional factors, such as marital status), and actual changes in the variable(s) of
interest (the ‘true’ or underlying change). The same applies when comparing data for two
or more populations at a single point in time. The techniques of standardisation and
decomposition are particularly suited to these tasks.
vii
The policy-maker’s guide to population ageing: key concepts and issues
viii
Understanding population ageing
1 Understanding population ageing
Almost every discussion of population ageing notes somewhere (either explicitly or implicitly)
that the phenomenon is the inevitable outcome of the demographic transition. Seldom,
however, is the latter itself explained. A simple overview of this phenomenon can assist in
demystifying many of its consequences.
The demographic transition in a nutshell
The most succinct description of the demographic transition comes from Paul Demeny
(1972), who stated that ‘in traditional societies, fertility and mortality are high. In modern
societies, fertility and mortality are low. In between there is the demographic transition’.
Although many would take issue with his dichotomy of ‘traditional and modern’, Demeny’s
description is very important for the way it draws attention to the period between the onset
and end of the transition. Prior to the onset of the transition, births and deaths are not only
high but are more or less in equilibrium—generally cancelling each other out—and
population growth is either low or static, sometimes slightly negative (see Figure 1, Stage I).
This was the case for most of human history. At the end of the transition, at least
theoretically, low to zero, potentially slightly negative population growth is again reached
(Stage III). But, during the transition (Stage II), populations grow in size, often explosively
(Coale 1972a, b).
Figure 1: The demographic transition (classic or western model)
Vital rates
Population growth
ZPG
Deaths
Births
ZPG
Stage I
?
Stage II
Stage III
Momentum effect
The growth occurs because, typically, the factor that heralds the onset of the transition is a
decline in infant mortality. As infant mortality falls ahead of fertility, more babies survive,
causing the population age structure (the numbers or proportions to be found at each age–
1
The policy-maker’s guide to population ageing: key concepts and issues
see Figure 2) to expand at its base and become structurally younger. Within 15–30 years,
typically before fertility has begun to fall significantly, most of these survivors have children
themselves, causing further population ‘juvenescence’ and expansion.
Once fertility begins to fall, the rate of population growth slows, but the population
continues to grow in size for several years, because the next typical occurrence is growth
from a phenomenon known as the momentum effect (Keyfitz 1971). The momentum effect
is the growth potential that remains contained within the age structure, after fertility has
begun to fall. Even if fertility fell immediately to the levels required for the exact
replacement of each generation (2.1 births per woman), populations generally continue to
grow in size for at least one generation, because each successive cohort reaching
reproductive age is typically larger than its predecessor. This is due to the past effects of
the higher fertility and falling infant mortality. The higher the pre- or early-transitional
fertility and the longer it takes to fall to replacement level, the longer the momentum
effect continues.
Immediately fertility does begin to fall, however, the population age structure begins to
mature, that is, to become structurally older. As fertility falls, the proportion of the
population at the younger ages decreases; concomitantly, the proportion at the older ages
increases. This is known as structural population ageing.
Theoretically, the reaching of replacement level fertility was supposed to herald the end of
the demographic transition. It would bring with it a return to the situation of zero
population growth noted above, or even incipient, intrinsic decline. The latter is a possibly
temporary period of population decline caused by deaths outnumbering births, the
outcome of increased numbers of elderly (see numerical ageing below) in relation to
falling fertility. However, in most of the more developed countries, fertility has either fallen
or is continuing to fall well below replacement level, and incipient decline is in danger of
becoming population implosion. In the absence of substantial net migration gains, almost
all industrialised countries are projected to decline in size over the next 50 years, some
dramatically (United Nations 2000). For many this phase has already begun.
With reference to these dynamics, population ageing is best understood by considering it as
having two technical dimensions: structural and numerical ageing.
• Structural ageing (an increase in the proportions of elderly) is primarily the result of
falling fertility. Falling infant mortality and increasing life expectancy are also involved, in
that they add to the numbers and thus proportions of elderly. However, they are not the
primary cause of structural ageing: a population will not age structurally while it has high
fertility. The latter reflects the situation that occurred during the baby boom (in Australia,
between 1946–1965),1 when mortality was low but fertility increased. The result was a
short-term juvenescence of the age structure, after several decades of ageing that had begun
in the 1880s.
2
Understanding population ageing
• Numerical ageing, on the other hand, is primarily caused by falling mortality. As infant
mortality declines, more babies survive, causing a spurt in population growth. Within a few
decades these babies become reproducers themselves, causing a further spurt in population
growth. Both cause an initial juvenescence of the age structure. As life expectancy improves
among the adult population (later in the demographic transition), those who survive infancy
and childhood have a high probability of reaching old and very old age. The high fertility
over the baby boom years will shortly become a major contributor to numerical ageing (and
to structural ageing), in that the numbers born then will begin to reach old age around
2010. However, this will not be the dominant cause. A population will not experience a
significant increase in the numbers of elderly if mortality is high, even when fertility has
been very high.
This distinction between structural and numerical ageing is very important for social and microeconomic policy. It is numerical ageing that is driving up the demand for and cost of income
support, health-care services, and so on, while it is structural ageing that is the constraining
factor. Structural ageing will soon2 mean a decline in the proportion of the population at
workforce age (that is, the primary tax base), when compared with the increased numbers of
dependent elderly, and a reduction in the ability of governments to fund these pensions and
services.
The distinction is also important because structural ageing is essentially reversible (that is
theoretically responsive to policy), while numerical ageing is not, at least in the short-term.
A sustained increase in fertility would cause an immediate reduction in the proportion of the
population at the older ages, and, after 18-20 years, an increase in the proportion at workforce
age. However, the benefits to the working-age population/tax base would not be realised for
those 18–20 years. On the other hand, a sudden or dramatic increase in fertility would create
an age structure akin to an hour-glass, dramatically increasing the total dependency ratio (the
ratio of 0–14 and 65+ year olds when compared with those aged 15–64 years (see Section 4
below). By contrast, the only way numerical ageing could be reversed would be via a net loss
of people at the older ages. All things remaining equal this will not happen until most of the
large baby boom and bust cohorts have died—around 2070–80.
Within this somewhat straightforward depiction of structural and numerical ageing is one
further important point. Many people confuse population ageing, or more correctly, structural
ageing, with the movement of baby boom cohorts through the age structure. The data
presented in Figure 2 below clearly show how the baby boom (shaded dark), which initially
created a triangular-shaped, youthful population pyramid, is moving upwards through the age
structure, augmented, since its birth, by migrants. This upwards movement has indeed
contributed to the slowly increasing median age of the population. From approximately 2009,
it will contribute to both the proportions and numbers of elderly. However, it is the smaller,
post-baby boom cohorts, with the significant exception of the larger, so-termed ‘baby bust’
cohorts born 1968–74, that are bringing about structural ageing, not the baby boom by itself
(see also Section 5, below).
3
The policy-maker’s guide to population ageing: key concepts and issues
1976
FEMALES
0.0
0.5
1.0
Percentage at Each Age
85+
80
75 MALES
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
1.0
0.5
1996
FEMALES
Age
85+
80
75 MALES
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
1.0
0.5
Age
Age
Figure 2: Age–sex structure of the Australian population, 1976, 1996 and 2016
0.0
0.5
Percentage at Each Age
1.0
85+
80
75 MALES
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
1.0
0.5
2016
FEMALES
0.0
0.5
1.0
Percentage at Each Age
Source:
Compiled by the author. 1976, 1996: ABS Census of Population and Dwellings; 2016: ABS 2000, Catalogue 3222.0, Series IIa.
Notes:
Dark shaded bands = baby boomers
The large cohorts born between 1968 and 1974, following the technical peak (1961) and end
(1965) of the baby boom therefore need some explanation. The baby boom is defined in terms
of the increase in the TFR or average family size that occurred between 1945 and 1965, not
the size of the resulting birth cohort, which is what is shown at each age-point in Figure 2.
Although average family size was indeed falling throughout the 1960s and ‘70s, the period often
referred to as the baby bust, an increase was occurring in the numbers of women arriving at
reproductive age. This reflected the first of the baby boom cohorts reaching this age. Because the
size of each birth cohort reflects the combined effect of average number of births and the
numbers of women at the key reproductive ages, these momentum effect dynamics caused
Australia’s biggest birth cohort to be born in 1971, not 1961 (see section 5, below).
4
The birth rate
2 The birth rate
Given the importance of trends in fertility for structural ageing, the next most important
concept to understand is the total fertility rate (TFR). The TFR is a synthetic estimation of
the average number of children a woman would expect to bear during her lifetime if she
were to experience all of the age-specific birth rates occurring in that year. This index, which
is calculated for women aged 15–49 years, is also sometimes called a ‘period rate’ because it is
based on births occurring during a given period (that is, a year). It contrasts with the
completed fertility rate (CFR) which is sometimes called the cohort fertility rate). The CFR
refers to the average number of children actually born to woman from a given cohort.
Because the CFR requires longitudinal data, it can only be calculated for women who have
reached their late forties. As a result of this time delay the TFR is used as an approximation
of the CFR (see McDonald 2000 for a detailed description). Importantly, neither the TFR nor
CFR permit identification of the number or proportion of women who are having no children,
the implications of which are discussed below.
Table 1:
Age-specific and total fertility rates, Australia 1986 and 1998
Age group
1986
1998
15–19
0.022
0.019
20–24
0.090
0.060
25–29
0.142
0.111
30–34
0.089
0.107
35–39
0.027
0.046
40–44
0.004
0.008
45–49
0.000
0.000
TFR
1.870
1.755
Source:
ABS Births 3301.0
Understanding the synthetic construction of the TFR is especially important for understanding
the limitations of the index. As shown in Table 1 above, the TFR is the sum of the age-specific
fertility rates. These are ratios of the number of births at each age to the number of women at
each age. When five year age groupings are used, as in Table 1, the result is multiplied by five,
to account for the width of the age band (five years). When single year-of-age data are used,
the age-specific rates are simply summed.
The major problem with the resulting index is that it can be heavily distorted by shifts in the
timing of childbearing—the average age at which women give birth. As Figure 3 indicates for
Australian women, this has changed dramatically across this century, falling from around
28.5 years in the 1930s, to 25.5 years around 1970, returning to nearly 30 years in 1999. An
upward shift in age at childbearing tends to lower the TFR, while a downward shift raises it.
De Beer et al. explain this:
5
The policy-maker’s guide to population ageing: key concepts and issues
If, at a certain point in time, increasing numbers of women decide to stop child-bearing…
then the total number of births will decrease due to the loss of third or higher order births.
If, at the same time, increasing numbers of women decide to postpone the arrival of a first
and/or second child to a later age, then the total number of births drops even further…
Summing up these age specific rates gives very low TFR values.
After a number of years the tide might turn. The women who postponed childbearing will
have grown older and may decide to [begin childbearing] at age 27, 30, or even later [and]
the fertility rates at these ages [will] start rising again. [If] at the same time, the youngest
generations …prefer to have their first and or second child at young ages again, then fertility
rates at these ages will also start rising. [T]heir sum, the TFR, ends up at a high level again.
(De Beer et al. 1991, p. 40)
All women in this (hypothetical) example had two children. Their completed fertility didn’t
change, only the age at which they had those children.
Source:
1996–98
1991–95
1986–90
1981–85
1976–80
1971–75
1966–70
1961–65
1956–60
1951–55
1946–50
1941–45
1936–40
1931–35
1926–30
30
29
28
27
26
25
1921–25
Median age
Figure 3: Median ages of mothers (all births), Australia 1921–98
Compiled by the author. ABS Catalogue 3301.0, Various Years
Understanding the distinction between the TFR and CFR is especially important because
when compared across time, the TFR is typically higher than the highest CFR, and lower than
the lowest CFR (see also (ABS Births 1999; and Wilson 1985, p. 221). The disparity tells us that
very high TFRs are likely to over-estimate average completed family size, while very low TFRs
under-estimate it. As Figure 4 shows, the point is especially pertinent to the cohorts who gave
birth to the baby boomers. The cohort born 1930, for example, experienced its peak
childbearing years during the late 1950s to early 1960s, when the TFR was peaking around 3.6.
According the CFR, the average completed family size for these women was fractionally above 3.0.
6
The birth rate
Figure 4: Total fertility rate 1921–99, and completed fertility rate for cohorts born 1905–60
lagged by 30 years, Australia
TFR
3.2
Source:
Notes:
CFR
2.8
2.4
2.0
Cohort born 1930
1997
1993
1989
1985
1981
1977
1973
1969
1965
1961
1957
1953
1949
1945
1941
1937
1933
1929
1925
1.6
1921
Average births per woman
3.6
Compiled by the author. ABS Catalogue 3301.0, Various Years
The latest completed fertility data available are for the cohort born 1950; data for cohorts born 1950-60 have been
estimated. The lagging of cohort data by 30 years permits comparison of the approximate TFR over the peak of these
cohorts childbearing, against the actual average family size.
The disparity between the TFR and CFR has both micro- and macro-level policy implications.
At the micro-level, the TFR under or overestimates such things as the number of children that
each family will be supporting, and how many children each generation of parents will have
to call on for support in their old age (see also the parent support ratio section 4, below). At
the macro-level, the age-specific fertility rates (of which the TFR is comprised) are used to
project the size and structure of the future population. Simply stated, current age-specific
fertility rates are applied to (multiplied by) the number of women projected to be at each age,
at each successive year (see section 12, below).
This calculation, when adjusted for mortality and migration, gives the number of new entrants
(births) to the population. If the number of new entrants to the population age structure are
over or underestimated, so too are the quantum and tempo of structural ageing. Indeed it
should be noted that the most recent ABS 2000 high-range projections are based on a TFR of
1.75 through to 2051. For the lower-range projections this drops to 1.60 after 10 years, and
remains constant across the projection period. Given that Australia’s TFR is already below the
upper level, is close to the lower level in the Australian Capital Territory and Victoria, and that
fertility is somewhat lower in many countries, the assumptions may be too high. If Australia’s
fertility does in fact fall below the assumed levels, as many demographers expect (McDonald
2000), structural ageing will be more pronounced and will occur faster than anticipated.
As also noted, a further very important point is that neither the TFR nor the CFR give an
indication of the number or proportion of women who are having no children at all. That is
to say, the TFR is an average for all women of reproductive age (15–49 years), while the
CFR is an average across women belonging to a specific cohort. Accordingly, in a context
7
The policy-maker’s guide to population ageing: key concepts and issues
where increasing numbers of women are remaining childless in Australia currently estimated
at 20 per cent (Merlo & Rowland 2000), a TFR or CFR of 2.1 or less indicates that many of
those who are still having children, are having more than two.3 As McDonald (1998) explains,
Australia’s 1996 TFR of 1.8 was being held up by the relatively high proportion of women still
having three or more children, around 25 per cent. However both age-specific and parity data
for each successive cohort indicate that this proportion is falling sharply. Whether the fall will
ultimately be mirrored in the CFR is open to conjecture, but, like the TFR, the CFR is likely to
remain below replacement level (Bongaarts 1999; McDonald 2000). In that case it will have
long-term implications for both structural ageing and population size (see also section 6, below).
8
Measuring life expectancy
3 Measuring life expectancy
As indicated in the previous sections, closely related to population ageing and its measurement
is another important concept: life expectancy. Life expectancy concerns the probability of
survival, and, similarly to the TFR (although quite differently constructed), is a synthetic
measure based on the age-specific death rates occurring in a given year. Typically, the term
life expectancy means life expectancy at birth (e o 0 ). However, because the most dangerous
days of life are the first and last, surviving the first days, weeks, months, and then year of life
generally results in an increase in life expectancy. At birth, a person’s life expectancy may be
60 years. But if that person survives to age 60, their remaining life expectancy (e o 60 ) will these
days typically be another 20 or more years. Thus, life expectancy for any given age specifies
the number of additional years the average person can expect to live. Table 2 illustrates this
phenomenon for Australians born in 1932. Males born in 1932 had, at birth, a life expectancy
of 63.5 years. For those who reached this age, a further 17.59 years had been added, giving an
average minimum life span of 81.1 years. The data for females can be similarly interpreted.
Table 2:
Life expectancy at birth, and on reaching that age, Australian cohorts born 1932
(aged 68 years in 2000)
Life expectancy
at birth
On reaching life
expectancy at birth
Average minimum
life span
Males
63.5
17.6
81.1
Females
67.0
18.2
85.2
Source:
ABS Deaths, various years
Notes:
For males, on reaching 63 years; for females, on reaching 67 years
Life expectancy at birth is also often confused with average age at death. From the above
discussion it can be inferred that the two measures relate to quite different populations and
situations. Life expectancy at any age refers to average years of life remaining for those born
in a given year (a birth cohort); average age at death refers to all those dying in a given
year (thus from many cohorts). The distinction is important in a policy-making context, for
example, in relation to projecting demand for Age Pension, because average age at death
(say, 80 years) will always be lower than the average life expectancy remaining (say, nine years)
for those reaching this age.
Life expectancy also differs substantially between men and women (typically between two and
eight years), and between people of different socioeconomic and ethnic backgrounds. These
understandings are critically important for policy makers endeavouring to determine future
demand for and access to pensions and services, health care, and so on. Sex-specific (and
possibly ethnic-specific) measures of life expectancy should be employed in, for example,
the rationale for setting the age of eligibility for access to certain goods and services of the
welfare state.
9
The policy-maker’s guide to population ageing: key concepts and issues
10
Indices of ageing
4 Indices of ageing
The most commonly used indicators of population ageing are the proportion of the
population aged 65 and over, and the median age (the age above and below which half
the population fall). Populations are considered young when less than 5 per cent of the
population is aged 65 and over (or more than 35 per cent is aged less than 15 years), and old
when this proportion reaches 10 per cent, although in developing countries where mortality is
still high, it is practical to take 60 years as the cut-off age. Similarly, a population is considered
young when it has a median age of less than 20 years, and old when this index reaches 30 years.
Populations in between these extremes are considered to be of intermediate age.
Table 3:
Median ages and projections of aged for selected countries and regions, 2001 and 2020
Median age
Proportion aged 65+
Change
2001
2001
2020
%
Japan
41.4
17.5
26.8
53.1
Germany
40.2
16.6
21.4
28.9
Italy
40.0
18.3
23.5
28.4
Greece
39.2
17.7
21.8
23.2
United Kindom
37.9
15.7
19.6
24.8
France
37.8
16.1
20.6
28.0
Canada
37.1
12.8
18.2
42.2
Hong Kong S.A.R.
36.5
10.7
16.1
50.5
United States of America
35.9
12.6
16.5
31.0
Australia
35.4
12.5
17.6
40.8
Singapore
33.9
7.0
10.3
47.1
New Zealand
32.7
11.5
15.1
31.3
China
30.4
7.1
11.8
66.2
Source: United States Census International Database
Currently (2001), the median age of the Australian population is 35.4 years, and approximately
12.5 per cent are aged 65 and over. Australia is therefore considered to be an old population.
However, as Table 3 shows, it is relatively young compared to the populations of several other
developed countries.
Other common indices or proxies of population ageing are the ‘dependency’ or ‘support’
ratios. Conventionally, four such ratios are recognised:
• youth: 0–14 year olds in relation to those aged 15–64 years
• aged: 65+ in relation to those aged 15–64 years
• total: 0–14 and 65+ year olds in relation to those aged 15–64 years
• potential support ratio (PSR): 15–64 years in relation to those aged 65+
11
The policy-maker’s guide to population ageing: key concepts and issues
Whether as indices of population ageing, dependency, or support, these measures are
extremely crude. They reflect a time when people (mainly males) entered the labour force at
age 15, left it at age 65, and were employed full-time between those two ages. Today, the
upper and lower boundaries delimiting the economically active population are much more
fluid, while many of those aged 15–64 years are in fact ‘dependent’, for example, the
unemployed and jobless, youth living at home, those people receiving illness, disability and
other support pensions, and those people studying full-time, and/or caring for others (mainly
women). More refined dependency ratios should be constructed depending on the uses to
which they are being put. The upper and lower boundaries should reflect, for example,
average age at labour force entry and exit, while for certain purposes the number receiving
more or less full income support should be removed.
With these limitations in mind, Figure 5 below, gives the crude dependency ratios for Australia
for the 0–19 and 65+ year age groups, compared with those aged 20–64 years. (Note that
international comparisons are typically based on a working-age population of 15–64 years.)
According to these indices, Australia’s total dependency ratio will reach its lowest point in
approximately 2009, after which time it will return quite rapidly to levels existing in the 1970s.
However, by contrast with the 1970s, the driving forces of this change are, predictably,
declining youth dependency and increasing aged dependency. This unprecedented change in
the composition of the total dependency ratio is very important to understand because of the
relatively greater costs associated with aged dependency, and because these costs are largely
borne by government. It is argued that the cost to the government of support for the elderly is
between two and four times that for children (Borowski & Hugo 1996, p. 49, who cite a
number of studies).
Actual
0.9
Source:
12
Projected
Total
0.8
0.7
0.6
Youth
0.5
0.4
Aged
0.3
0.2
0.1
Compiled by the author.
1971-1998: ABS Population Estimates; 1998-2051: ABS 2000, Catalogue 3222.0, Series IIa
2051
2047
2043
2039
2035
2031
2027
2023
2019
2015
2011
2007
2003
1999
1995
1991
1987
1983
1979
1975
0.0
1971
Ratio to persons aged 20–64 years
Figure 5: Youth (0–19 years), aged (65+ years) and total (0–19 and 65+ years) dependency ratios,
Australia 1971–2051
Indices of ageing
Concealed within these indices is also the fact that between 2011 and 2051 the proportion of
the Australian population aged 20–64 years, the primary tax base, is projected to decline from
its peak of just over 61 per cent, to around 54.4 per cent. For the population aged 15–64 years,
these figures are 68.1 per cent in 2009 and 59.6 per cent in 2051. The PSR, which is widely
used in United Nations analyses, is illustrative of the impact. Currently sitting at 4.9 persons
aged 15–64 years to each person aged 65 and over (having fallen from 6.5 in 1972), the ratio
will fall rapidly to 3.0 by 2024 and 2.1 by 2051 (ABS Series IIa).
Two other very useful indices of population ageing are the aged/child and parent support ratios.
The former measure directly compares the two age groups (0–14 and 65+ years) that undergo
the most change during demographic transition. Because this measure is the most sensitive to
changes in the age composition, it is conventionally considered the best index of ageing
(Stockwell 1976). As Table 4 shows, for Australia this index will decline from 1.7 children per
person aged 65+ in 2000 to 0.6 in 2050. According to these projections, the two will be briefly in
balance around 2016–18; thereafter those aged 65+ will outnumber those aged 0–14 years.
Table 4:
Projected aged/child and parent support ratios, Australia 2000–50
Aged/child
Ratio (1)
Parent support
Ratio (2)
2000
2005
1.7
1.5
2.4
2.3
2010
1.3
2.2
2015
1.1
2.0
2020
0.9
1.8
2025
0.8
1.5
2030
0.7
1.2
2035
0.7
1.1
2040
0.6
1.0
2045
0.6
0.9
2050
0.6
0.9
Source:
Compiled by the author
ABS Population Projections 2000 Series IIa
Notes:
(1) 0–14 years : 65+ years
(2) 45–54 : 75+ years
NB. When constructing the parental support index it is important to keep intergenerational shifts in the timing of family formation
in mind, and also the fact that not all adults have children.
By contrast, the parent support ratio measures the relative size of offspring (for example,
45–54 years) and ‘parental’ (75+ years) cohorts to approximate potential family support
available to the elderly. (The latter is also sometimes termed the parent/progeny ratio. It differs
from the potential support ratio described above in that it is based on relational age groups.)
This index will similarly decline from 2.4 in 2000 to 0.9 by 2050. Note that, like the TFR and
CFR discussed earlier, this ratio implies universal childbearing, whereas in reality a proportion
of adults never had children.
13
The policy-maker’s guide to population ageing: key concepts and issues
14
The birth rate, cohort size, population ageing
5 The birth rate, cohort size, population ageing
The concepts of birth rate, cohort size, and population ageing are often used interchangeably
and incorrectly.
As outlined above, the birth rate, whether calculated as the TFR or the CFR, is an index used
to approximate average family size. It has a number of limitations, not least that it conceals
the extent to which an increasingly large proportion of people are not having children.
A cohort, on the other hand, is a group of people connected by a similar event. This may be
birth in a given year (which derives a birth cohort), marriage in a given year (a marriage
cohort), death (a death cohort), or even a war (those who were young adults between 1939
and 1945 are sometimes referred to as the war cohort). Cohort size in relation to a birth cohort
refers to the number of people born in any given year, later augmented by immigration or
reduced by emigration and death.
Despite the apparently clear distinction between the two concepts, they are often confused. For
example, much attention has been directed towards the large cohorts born during and
especially at the end of the baby boom. At its peak (in 1961), the TFR was 3.6 (and cohort size
239,986). However, as noted earlier, the cohort born in 1971 was considerably larger
(n=276,361). This occurred because, although fertility had by then fallen to 2.9 births per
woman, there were more women giving birth, the first of the baby boom generation having
arrived at reproductive age (the momentum effect as outlined in section 1, above). In other
words, cohort size (the number of births in any year) is the combined function of the birth rate
and the number of women of reproductive age (and, of course, actually having children).4
The distinction between the two concepts (cohort size and the birth rate) is clearly illustrated
in Figure 6, as is the momentum effect. The outcome of the momentum effect is that most of
those born during the so-called ‘baby bust’ (1968–74) in fact belong to cohorts that were, and
in most cases remain, larger than their baby boom parents and predecessors. Indeed ‘baby
bust’ should be considered a misnomer.
Despite similarly clear technical distinctions, changes in cohort size are also often confused
with population ageing. In particular, as noted earlier, the movement of the baby boom
cohorts through the age structure is often referred to as population ageing. However, as
explained, the changes in cohort size that occurred during the baby boom were part of a
short-term shift to a younger population, not an older one. Also, seemingly paradoxically,
since most of the baby bust cohorts are larger than the baby boom cohorts, population ageing
will not only continue once the baby boomers have reached very old age and begun to die,
but may even accelerate. This will depend upon what happens with fertility in the meantime.
Accordingly, structural ageing may be better conceptualised as a function of declining cohort
size, than declining birth rate.
15
The policy-maker’s guide to population ageing: key concepts and issues
Figure 6: Total fertility rates and cohort size, Australia, 20th Century
300,000
Momentum effect
3.5
250,000
TFR
3.0
200,000
2.5
2.0
150,000
1.5
100,000
Cohort size
1.0
50,000
0.5
Source:
Notes:
1996
1991
1986
1981
1976
1971
1966
1961
1956
1951
1946
1941
1936
1931
1926
1921
1916
1911
1906
0
1901
0.0
Number (cohort size at birth)
TFR (average births per woman)
4.0
Compiled by the author
ABS Australian Demographic Trends 1997 Appendix 16; ABS Births, various years
Data exclude Aboriginal and Torres Strait Islander Population prior to 1966
According to demographic transition theory, significant fluctuations in cohort size are not
expected to re-occur once the demographic transition is complete (Coale 1972 a, b). This is
because, theoretically, the population age structure will reach the state of zero population
growth noted earlier (births and deaths will be more or less equal) and become stable. The
proportions at each age will not change appreciably from year to year. As could be seen in the
panel for 1996 in Figure 2 (see section 1 above), significant fluctuations in cohort size at birth
have already ceased to occur.
However, as was also implied, projections assuming zero growth and population stability are
dependent on one very important factor—fertility returning to and remaining around a TFR of
2.1 births per woman, the theoretical replacement ratio. Currently, approximately 60 of the
world’s populations have fertility lower than this. In Continental Europe, for example, the TFR
ranges between 1.1 and 1.4 (United Nations 2000). If fertility fall to these levels in Australia,
newly born cohorts will continue to decline in size, and structural ageing will accelerate.
These points aside, for both policy makers and analysts, changes in cohort size will remain
very important for some time. They are, at this moment in Australia, more significant than
population ageing. Two examples will suffice. First, the cohorts currently (2001) entering the
elderly population are those born in the 1930s, and are smaller than either their predecessors
or successors (see Figure 6). According to Borowski and Hugo (1996):
16
The birth rate, cohort size, population ageing
this group’s passage through the older ages will lead to a significant reduction in the pace of
ageing in Australia in the 1990s and early twenty-first century …However, rapid growth of the
elderly population will recommence and reach unprecedentedly high rates when the
post-war baby boom children begin to enter the retirement ages after 2011 (p. 27).
Figure 7 illustrates the situation using data for the 55–64, 65–74 and 75+ age groups. Over the
next decade the population aged 55–64 years will grow at a considerably faster rate than the
population aged 65–74 years. This is because the first of the baby boomers are now entering
the former group. Thereafter, as they leave the first group and move into the second, the
population aged 65–74 will grow at the faster rate. Finally, as the baby boomers reach the 75+
age groups, the latter population will grow at the fastest rate. They will outnumber both the
55–64 and 65–74 year age groups by between 2030 and 2035. The magnitude of the shift will
be nothing short of remarkable, with the 75+ group growing from 1 million at present to more
than 3.5 million by 2051. Within these broad age groups, trends for individual age groups are
even more pronounced. At the older ages, significant differences between each sex should
also be noted: at each successive age, women increasingly outnumber men.
Figure 7 clearly illustrates the importance of disaggregating the elderly population. Not only
will there be successive waves of elderly, but each wave will differ from its predecessors
(Mackay 1997). Indeed, when considering distinctions between cohort size and population
ageing, one further distinction, that between the cohort and the age group, is also warranted.
Over time, cohorts age (the people in a birth cohort grow older); age groups do not (people
pass into and out of them). As a result, the waves of elderly age groups contain cohorts that
have had very different life experiences (especially among women), including differences in
education, income, savings behaviour, labour force attachment and childbearing. These
differences pertain not only to level, but also to timing. As Easterlin (1988), Hagenaars (1990),
MacKay (1997) and others have argued, each cohort encounters certain period events and
circumstances (such as a depression, economic boom or restructuring) at a different age. This
nexus has the potential to develop into cohort effects. For example, cohorts that encounter a
situation of full employment around labour force entry age, such as the cohorts born in the
1930s, may experience higher lifetime levels of employment and savings potential than cohorts
that experience the opposite. Such differentiated cohorts deal with each life stage in different
ways, and are likely to require (and demand) quite different retirement experiences.
17
The policy-maker’s guide to population ageing: key concepts and issues
Figure 7: Projected increase in populations aged 55–64, 65–74 and 75+ years, Australia
Number in each age group
4,000,000
3,500,000
55–64 years
3,000,000
2,500,000
65–74 years
2,000,000
75+ years
1,500,000
1,000,000
500,000
Source:
2051
2048
2045
2042
2039
2036
2033
2030
2027
2024
2021
2018
2015
2012
2009
2006
2003
2000
1997
0
Compiled by the author
ABS 2000, Catalogue 3222.0, Series IIa
Each Australian cohort is also differentiated ethnically, with high proportions (around
40 per cent) of the oldest cohorts born in the United Kingdom/Eire and Europe (Hugo 1988,
see section 8, below). More recently born cohorts have higher proportions of, for example,
people born in Asian countries. The implications of important information such as this are
rendered invisible when trends in ageing are analysed by age group only.
The second example concerns cohorts currently at the younger end of the age spectrum. It has
been argued (Easterlin 1988 and others) that large cohorts experience greater intra- and intercohort competition for available resources (such as education, jobs and income) than do small
cohorts. As a result, large cohorts are likely to have a more negative labour market and
earnings experience, and, subsequently, to have later and lower fertility, than small cohorts.
Potentially substantiating the argument, both the extremely large cohort born in 1971, and
those immediately surrounding it, have been strongly affected by unemployment, and have the
lowest and latest fertility to date.
The implications of the situation are manyfold. For example, as this very large cohort leaves
behind the high youth unemployment years, as it has recently done, the employment earnings
situation is expected to improve for its successors. Described as a youth deficit by the
American Central Intelligence Agency (CIA 1990),5 the situation of declining proportions of
youth is expected to see an increase in global competition for the labour and skills of young
people. Certainly as Figure 8 shows, labour market entry-exit ratios for Australia are now
falling rapidly. Currently just on one 18–24 year old is at labour force entry age for each
55–64 year old reaching retirement age and beginning to leave; by 2018 this ratio will fall to
0.8; and by 2018, below 0.7. These factors need to be borne in mind when analysing or
attributing findings to particular policy innovations that are attempting to reduce youth
unemployment. At least part of the reason for a decline in unemployment could simply be a
function of population ageing (see section 13 for methodological implications).
18
The birth rate, cohort size, population ageing
The positive implications of this trend notwithstanding, the earlier point that cohorts carry their
accumulated experiences with them should be kept in mind. It should also be noted that the
large cohorts born around 1971 are currently entering their main childbearing years. Despite
low and still falling fertility, this shift could herald a small increase in the number of births,
reflecting a momentum effect, and a concomitant increase in demand for child-related goods
and services such as paediatricians, schooling, and family-related payments
Figure 8: Labour market entry-exit ratios (18–24:55–64 years), Australia 1971–2051
1.6
Observed
Projected
1.4
Ratio 18-24:55-64 years
1.2
1.0
0.8
0.6
0.4
0.2
Source:
2051
2047
2043
2039
2035
2031
2027
2023
2019
2015
2011
2007
2003
1999
1995
1991
1987
1983
1979
1975
1971
0.0
Compiled by the author
1971–98 ABS Population Estimates; 1997–2051: ABS 2000, Catalogue 3222.0, Series IIa
19
The policy-maker’s guide to population ageing: key concepts and issues
20
Natural increase and decrease; doubling and halving time
6 Natural increase and decrease; doubling and halving time
Many people are familiar with the term ‘natural increase’ (technically called intrinsic growth
because it occurs within a population, as opposed to externally from migration). This is
simply the difference between births and deaths. Over the past two hundred and fifty years,
that is, since the onset of Stage II of the demographic transition, the natural increase
component of population change has become taken for granted.
Since the 1950s, when many of the developing countries began their transition, concerns with
the global rate of growth in natural increase (RNI) have become associated with the concept of
‘doubling time’—the time it takes for a population to double in size. As a rule of thumb this
index is estimated by dividing 69.3 years by the annual rate of growth (Weeks 1999, p. 11).
Between 1950 and 1985 this gave a world population doubling time of about 35 years. As
outlined in section 1 above, the main reason for the dramatic rate of growth was not, as many
believe, high or increasing fertility in the developing countries, but falling infant mortality,
which saw more babies survive and natural population growth compound.
With fertility now also falling in most developing countries, the momentum effect described in
section 1 is under way, resulting, for most, in massive population growth, but growth that is
occurring at a decreasing rate. Indeed, the deceleration in the world’s population growth rate
is nothing short of astonishing, from 2.0 per cent per annum in 1970, to 1.3 per cent in 2000,
deriving a current doubling time of greater than 50 years.6 In addition, in many developing
countries HIV/AIDS is expected to cause an increase in mortality rates over the next two
decades, as well as decimate reproductive age populations, with a loss of the children they
would have borne (U.S. Bureau of the Census 1999). For these reasons, world population
projections are being constantly revised downwards, with numbers in most developing
countries expected to peak and begin to decline towards the end of this century (see also Lutz
1994, 1996).
As implied earlier, this opposite trend towards intrinsic (natural) decline, and potential
concerns with population halving time, in the developed countries is well established. As
McDonald (1998 p. 3) explains, its dynamics are simply the obverse of the above. Just as a
young age structure contains a momentum of population increase, so too an old age structure
contains a momentum of population decline:
If women, on average, have just one child …then the size of the generation will halve in
one generation that, in demographic terms, is about 28 years. In 56 years, the generation
size will only be a quarter of what it was two generations beforehand.
At the same time as the decline in fertility is driving down the number of births, the increasing
numbers of elderly are driving an increase in the number of deaths.7 With the two trends on a
collision course, the likely outcome is a cross over, and natural decline. Figure 9 illustrates the
situation for Australia, where natural decline is projected to occur during the third decade (see
section 8 below for regional differences; see also ABS Births 2000). It should be noted that
these data include migration at the medium variant assumption.
21
The policy-maker’s guide to population ageing: key concepts and issues
Figure 9: Births and deaths, Australia, 20th Century, and projected
Observed
Projected
300,000
300,000
250,000
250,000
Deaths
200,000
150,000
150,000
100,000
100,000
50,000
50,000
Source:
2044
2036
2028
2020
2012
2004
1996
1988
1980
1972
1964
1956
1948
1940
1932
1924
1916
0
1908
0
Number
Number
Births
200,000
Compiled by the author
ABS Catalogue 3301.0, Various Years; ABS 2000, Catalogue 3222.0, Series IIa
The extent to which individual populations will actually, rather than theoretically, decline, is
difficult to determine, because as natural decline approaches it is likely that extra efforts will
be directed at stabilising the birth rate (see section 11 below). Also, at least in the short term,
increased migration is likely to be used to ameliorate the impact in countries such as Australia
(see section 7 below for the feasibility of this option). However, what is singularly important to
understand is that the shift to natural decline is not a cyclical trend. The one-off natural growth
that accompanied the demographic transition is now over for the developed countries, and is
expected to be over for the developing countries before the century’s end. Furthermore, if the
birth rate continues to remain substantially below replacement level (2.1 births per woman) or
declines further, intergenerational halving time has the potential to become total population
halving time. An overall growth rate of -0.5 per cent would derive a halving time of 140 years;
-1.0 per cent, 70 years, and so on. Such a situation would cause a further dramatic upward
shift in the age structure (hyper-ageing), and, among other things, a concomitant incapacity to
sustain a social security system of the type Australians currently enjoy. This latter is, of course,
in the absence of social and economic changes that would, for example, increase productivity
or delay retirement.
As will be elaborated in the following section, such scenarios are not merely conjecture.
Current fertility levels in Germany (TFR 1.4) for example imply a negative rate of natural
increase (in other words, natural decline) of –1.7 per cent. If maintained for 200 years, in the
absence of a substantial increase in migration, such a rate would shrink the German
population to one-thirtieth its current size (Demeny 1986, p. 153). Similarly, with reference to
Italy (TFR 1.2), McDonald (1998, p. 3) explains that ‘once the impact of the crude birth rate on
the current age structure has been wiped out (in about 40 years), the [Italian] population in the
subsequent 100 year period would fall to just 14 per cent of its current level’.
22
Is migration the answer?
7 Is migration the answer?
Migration is often proposed as the answer to population ageing. That is, because migrants are
typically concentrated at younger ages than the host population, a net gain from international
migration is argued to assist in keeping a population young; or, more accurately, in keeping
the labour force (and primary tax base) from declining in proportion to the elderly population.
More recently, the emerging reality of natural decline has come to the forefront of the debate,
resulting in an awareness that in the near future, replacement migration8 (United Nations
2000) will have to address three issues:
(i)
maintenance of the size of the total population;
(ii)
maintenance of the size of the working-age population; and
(iii) maintenance of the ratio of working-age to elderly.
The arguments have been broadly debated, but the general consensus is that migration will be
hard pressed to solve the emerging problems (United Nations 2000).
First, the numbers required to offset structural ageing are enormous. Table 5 below, shows
United Nations projections for a selected range of countries expected to undergo extreme
ageing and intrinsic decline during the next 50 years. Even with the addition of sizeable
numbers of migrants at the medium variant assumption level, these projections show Germany
declining by just under 9 million (11 per cent) by 2050, Italy by 16 million (28 per cent), and
Japan by 22 million (17 per cent). In order to keep the Italian population at its current size,
Italy for example would have to take in a net 251 000 migrants per annum. This is many times
greater than Italy’s historical experience (the medium variant for Italy is 6 000 a year). This
level of net intake would total approximately 12.5 million migrants over the period. To
maintain the Italian working-age population at its current size, that intake would have to be
around 372 000 per year (a net of 19 million across the period); and to maintain the current
ratio of working-age to elderly (the potential support ratio), the net number of migrants
needed would be in the vicinity of 2.3 million per annum, or 113 million across the period.
This amounts to twice the current population, and few of whom would be ‘Italians’.
Table 5:
Current and projected size, and annual net number of migrants to achieve scenario
outcomes, by selected country or region and scenario
Current size
(2000)
I
Medium
variant
migration
82,220
57,298
204
6
73,303
41,197
344
251
487
372
3,630
2,268
Scenario
Country/region
Germany
Italy
Japan
II
III
IV
Projected
Constant
Constant
size 2050
total
age group
medium
population
15–64 years
variant *
size
Thousands (per annum)
V
Constant
ratio 15–
64/65+ years
126,714
0
104,921
343
647
10,471
United Kingdon
58,830
20
56,667
53
125
1,194
European Union
375,276
270
331,307
949
1,588
13,480
Source:
Notes:
United Nations 2000, Tables 1, IV.14, IV.19, V.22,
* Includes migration at the medium variant assumption
23
The policy-maker’s guide to population ageing: key concepts and issues
Kippen (1999) illustrates the structural aspects of the argument for Australia. Currently,
12.5 per cent of the population is aged 65 years and over. Under conditions of zero net
migration, and the TFR falling to 1.65 by 2008 and then remaining constant across the
21st Century, the percentage aged 65 and over would increase to 32.6 per cent. With the same
fertility assumptions, and annual net migration gains of 80 000, the proportion aged 65 and
over in 2098 would be reduced by a mere 4 percentage points, but 10.8 million would have
been added to the population (over the zero net migration scenario).9 A net migration gain of
160 000 per year with similar fertility would reduce structural ageing in 2098 by a further
1.6 percentage points (to 26.9 per cent), but in total would add 15.4 million to the population.
Demonstrating these trade-offs, McDonald and Kippen (1999, p. 14) have developed a very
useful index of efficiency (see box below), which shows the gain in numbers for each
percentage point reduction in aged population.
McDonald and Kippen’s index of efficiency
‘The index of efficiency measures the population increase resulting from the migration
changes required to reduce the proportion of the population aged 65 years and over by
one percentage point. For example, a shift in annual net migration from zero to 50,000
would reduce the proportion aged 65 years and over by 3.05 percentage points by the
year 2098. The same change would produce an increase in the total population over the
same period of 6.72 million. Hence:
Index of
Efficiency
=
Population increase due to change
Reduction in percentage aged 65+
=
6.72 million
3.05
= 2.2 million
This means that, with this change in the level of migration, a one percentage point
reduction in the aged population can be obtained at the cost of an addition to the size of
the population of 2.2 million people. An efficient change would be one that minimised the
increase in the population for each one percentage point reduction in the proportion of
the population aged 65 years and over.’ (McDonald and Kippen 1999, p. 14)
Summarising, Kippen (1999, p. 22) argues, first, that ‘if we wish to minimise the proportion
aged 65 plus and limit population growth, maintaining the birthrate is more efficient than
increasing migration’ (see also McDonald & Kippen 1999). She shows that a scenario of zero
net migration across the century and a rise to near replacement level fertility by 2008 would
see the proportion over the age of 65 in 2098 being around 26.4 per cent (compared to
32.6 per cent with the TFR of 1.65), against a total population of around 21 million. It is
interesting to observe that despite their growing concerns with natural decline, few of the
European Union countries are as yet preferring the migration option, focussing their efforts
instead on raising or maintaining their birth rates (United Nations 1999).
24
Is migration the answer?
Second, it is unlikely that the Australian birthrate will be raised, or even maintained, at least in
the short term, and especially through immigration. Not only is Australia’s TFR expected to fall
towards that of similar countries within a decade, but increasingly, the births of Australian
immigrants are also trending toward these patterns and levels. The fertility of several
immigrant groups is already lower than that of the total population, thus adding to structural
ageing (Abbasi-Shavazi 1998; Abbasi-Shavazi & McDonald 1997). Furthermore, a number of
commentators have argued that trying to create a fertility increase through pro-natalist policies
is less desirable than encouraging migration, because it takes many years for the effects of an
increase in the birthrate to have an impact on the population of economically active young
adults, while migration has an immediate effect (Heer 1986; Simon 1984; see also Höen 1987
on Europe).
Third, the problem with the latter argument, aside from the massive numbers that would be
required, is that because migrants also age, they add to the problem of population ageing in
the longer term (Young 1989, 1990; United Nations 2000). This point has been convincingly
demonstrated for Australia. Kippen and McDonald (2000), for example, show that Australia’s
current age structure is almost identical to what it would have been, had there been no net
migration gain since 1945 (see also McDonald & Kippen 2000; see also Le Brass 1991).
Clearly, these arguments and their associated trends and patterns have significant implications
for Australia’s future. With substantially higher fertility and per capita net migration gains than
most of her counterpart countries, and natural decline not projected to begin until the third
decade, Australia’s immediate situation is not as dire. From around 2030, however,
replacement migration will need to be pursued in earnest, if Australia’s population size is to be
held constant (see McNicoll 2000 for a critique of this ‘imperative’). However, Australia’s
previous sending countries are those that are already or imminently anticipating intrinsic
decline. Many have already become receiving countries, and others, such as Japan, which has
had very little experience of immigration, are now faced with this option or with its economic
consequences.
As the United Nations (2000, p. 22) points out, the European Union and the United States,
currently the world’s two largest economic bloc, are projected to follow starkly contrasting
demographic paths in the near future. By 2050, the population of the European Union will
have declined in size by around 41 million, while that of the United States will have increased
by around 82 million (however, it should be noted that it will also have peaked and be
beginning to decline). The result will see the population of the United States, which in 1995
was 105 million smaller than that of the European Union, exceed the latter by 18 million. The
economic and political implications of such divergence are large.
Thus, although migration is a poor counter to population ageing by itself, when considered in
the concomitant context of intrinsic decline, it becomes obvious that it will be one of the major
policy issues, if not the major issue, of the 21st Century. The feasibility of formulating and
adopting suitable migration policies poses enormous challenges for governments that decide
to pursue this option. Competition for migrants will be extreme. Moves to boost population
growth will result in, among other things, massive and more rapid changes to the ethnic
composition of host countries than previously experienced. Australia’s future migrants will
25
The policy-maker's guide to population ageing: key concepts and issues
almost certainly be ethnically different to those of the past. Along with such changes will come
enormous cultural, social, economic and political changes to both host and donor countries, not
least because the sought-after migrants are highly likely to be the educated young of the
developing countries.
26
Sub-population differences
8 Sub-population differences
One often overlooked yet extremely important point concerning population ageing is that the
extent and velocity of ageing may not be equal for all sub-populations, such as ethnic groups,
or regions, within the total population.
Unfortunately, lack of appropriate data makes it impossible to construct true age structures for
most ethnic groups.10 However, the significance of the phenomenon can be illustrated by
comparing data for the indigenous Aboriginal and Torres Strait Islander and total Australian
populations. As Figure 10 shows, Australia’s Indigenous population has a considerably
younger age structure than the total population: the median ages of the two populations are,
respectively, 20 and 35 years. These differences mean that as a proportion of each
population there are almost two Indigenous children (0–14 years) for each non-Indigenous
child, and at 15–24 years, 1.3.
By and large, the difference between the two populations reflects the higher fertility and more
recently, though also more slowly, falling infant mortality of the Aboriginal and Torres Strait
Islander population. But it is also partly classificational: according to ABS definitions, an
Aboriginal or Torres Strait Islander is any person who claims descent from an Aboriginal or
Torres Strait Islander, and is accepted as such by the Aboriginal or Torres Strait Islander
community in which he or she lives. This definition means a potentially exponential growth in
the number of births attributed to the Indigenous population (ABS 3230.0, 3231.0).
Figure 10: Age-sex structures of the Aboriginal and Torres Strait Islander and total Australian
populations, 1996
Aboriginal and Torres Strait Islander population
Males
70–74
Females
60–64
60–64
50–54
50–54
Age group
Age group
70–74
Total population of Australia
40–44
30–34
Females
40–44
30–34
20–24
20–24
10–14
10–14
0–4
0–4
8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8
Percentage at each age
Males
8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8
Percentage at each age
Source: Jackson 1999, Figure 2.2
27
The policy-maker’s guide to population ageing: key concepts and issues
Indigenous population numbers are also highly likely to be affected by the phenomenon of
category jumping, whereby individuals of mixed descent identify differently, often
inadvertently, between censuses and various data collections. According to Gardiner and
Bourke (2000), a sizeable proportion of this unexplained growth can in fact be explained by
reference to historical factors, such as the suppression of Aboriginal identity through the
stolen generation and its subsequent reclaiming in recent years (see also Pool 1991 on the
New Zealand Maori).
These and other identificational and classificational issues are very important for the policy
maker and analyst to engage with. How the boundaries of a group are technically defined
affects the size, structure, and growth rate of the group, with important implications for
equitable resource allocation and so on. The rapidly increasing number of Indigenous children
and young adults poses a significant social and economic policy challenge, in terms of
resources to meet their educational, employment, family formation, housing, and health needs.
If these needs are not met—if, for example, there is no recognition of the resource needs of a
youthful population existing within the midst of a total ageing population—Indigenous
marginalisation is likely to increase.
Also of importance is that such markedly differing age structures can inadvertently result in (or
conceal) discrimination, through policies that may be ‘ethnically-neutral’ on the surface
(Jackson 1994, 1998a). A policy that, for example, raises the age of eligibility for the adult rate
of an unemployment-related benefit, is likely to have a disproportionately negative impact on
a younger population. So too is a policy such as mandatory sentencing, given that a younger
population is disproportionately exposed to the risk of the type of activities that see young
people arrested. (These points are equally pertinent to regional differences in age structure,
which are discussed below.)
Despite the difficulties in determining the age structures of Australia’s immigrant groups,
country-of-birth data do provide an indication that is useful for policy purposes (see Table 6).
The extremely high median ages of the European-born populations, which also comprise the
largest ethnic groups among those aged 65+, should be especially noted.
A breakdown of these data by sex also indicates that, by contrast with the total population,
some immigrant groups (particularly Italian, Polish, Greek, Dutch, and former Yugoslavian)
have higher proportions of elderly males than females (currently affecting 75–84 year olds).
This may reflect lower levels of marriage earlier on. These points are even more pertinent
when English-speaking ability is considered. Approximately one in five older Australians was
born in a non-English speaking country, and a significant proportion, which is known to
increase as people age, is unable to communicate effectively in English. Hugo (1998) has
shown that this phenomenon affects mainly female immigrants, because while most male
immigrants of the time worked alongside English-speaking Australian’s and learned the
language, their wives remained at home to raise children.
28
Sub-population differences
Table 6:
Median age and percentage of Australian population aged 65 years and over, by
birthplace, 1981, 1991 and 1999
Median age
Percentage 65+
1981
1991
1999
1981
1991
1999
Italy
46
56
60
11.2
21.3
38.0
Greece
42
51
57
21.5
37.2
25.0
Netherlands
42
50
55
6.7
11.5
29.0
Former Yugoslav Republic
39
46
54
9.9
20.0
16.0
Poland
58
54
54
7.8
15.2
39.0
Germany
40
48
53
16.9
19.7
25.0
United Kingdom/Ireland
41
46
50
5.5
9.9
22.0
Phillipines
29
33
37
7.2
6.4
5.0
New Zealand
28
33
37
0.7
4.1
6.0
Viet Nam
22
30
36
2.0
2.6
6.0
Malaysia
24
31
32
0.5
3.3
4.0
Australian-born
26
29
31
9.2
10.4
11.0
Total Australia
30
33
35
10.0
11.0
12.0
Source: ABS 1981 and 1991 Censuses; 1999: ABS Migration Cat. No. 3412.0, pp. 83, 88.
With the non-English speaking background group currently increasing at a faster rate than the
mainly English-speaking aged, it is important to reflect on the extent to which an aged care
system developed by and for a primarily English-speaking population, can respond to the
changing population’s needs (Hugo 1988, p. 33). Hugo cites Bertilli (1980, in Ware 1981, p. 95)
as arguing: ‘it is of no use to an elderly person in need of constant supervision and care to be
admitted into a nursing home where he or she cannot easily communicate with
staff…psychologically and mentally it would be devastating: it would mean that the elderly
person has entered a tomb before the time of death’.
Also, as noted earlier, just as the size of each birth cohort may differ and create waves of
population, so too changes in the sending countries of migrants may create waves of ethnicity
that are characterised by age and cohort (see Figure 11). The shifts have implications for the
type of services that are and will in the future be needed by immigrants. Significantly, the data
indicate that the early post-war migrant populations are needing aged-related assistance and
resources now, not after 2010 when the ageing of the total population begins in earnest. By
contrast, the elderly of the future (say 2030) will be disproportionately Asian.
Differences between regional age structures have equally significant policy (and economic)
implications. As Figure 12 indicates, the populations of South Australia and Tasmania are
substantially older than those of the Australian Capital Territory and the Northern Territory, and
are projected to age at a faster rate. Tasmania will take over from Australia as the oldest State
around 2016, and the gap between the two will slowly increase. By 2051, the Northern
Territory will have a smaller proportion over the age of 65 than either South Australia,
Tasmania, or the Australian Capital Territory have at present.
29
The policy-maker’s guide to population ageing: key concepts and issues
Figure 11: Percentage of each age group born overseas, by region of birth, 1998–99
40
35
Percentage
30
25
20
15
10
5
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
0
Age
Source:
UK/Eire/New Zealand
Greece
Germany
Italy
Former Yugoslav Republic
Asia*
Africa (excl. North Africa)
Balance of Overseas-Born
Compiled by the author
ABS Catalogue 3412.0, 1998-99, Table 6.3
*Asia = North-East, South East, Southern, Middle East/North Africa
Notes:
Figure 12: Projected percentages aged 65+ years, selected States and Territories
30
25
20
15
10
5
Tasmania
Source:
30
South Australia
Australian Capital Territory
Compiled by the author
ABS Population Projections 1997-2051, Catalogue 3222.0, Series IIa
2051
2048
2045
2042
2039
2036
2033
2030
2027
2024
2021
2018
2015
2012
2009
2006
2003
0
2000
Percentage aged 65+ years
35
Northern Territory
Sub-population differences
Figure 13 illustrates these differences in terms of the projected decline in the rate of natural
increase (births minus deaths). Although Australia as a whole is projected to begin natural
decline some time during the mid 2030s, it is very clear that Tasmania and South Australia will
be experiencing this phenomenon much earlier (it should also be noted that these data are
based on the ABS Series Ia projections, which is the ‘best case’ scenario (see section 12,
below). Natural decline is not expected to occur in Victoria and New South Wales until the late
2030s and 2040s respectively, while Queensland, Western Australia, the Australian Capital
Territory, and the Northern Territory are not projected to go into natural decline before 2051.
Figure 13: Projected rate of natural increase and decline (per 1 000 persons), by State and
Territory
15
Per 1,000 population
10
5
0
-5
NT
ACT
NSW
SA
WA
QLD
VIC
TAS
2051
2046
2041
2036
2031
2026
2021
2016
2011
2006
2001
2000
-10
Source: Compiled by the author
ABS Population Projections 1997-2051, Catalogue 3222.0, Series Ia
31
The policy-maker’s guide to population ageing: key concepts and issues
32
Demographic compression
9 Demographic compression
Demographic compression refers to the inter-generational phenomenon that occurs when a
number of key demographic events are compressed into a shorter space of time, due to
generational changes in the age at which women give birth; the age at which children become
independent of their parents; trends in labour force entry and exit ages, and so on (Sceats;
Young 1990; McPherson 1992; Jackson 1998b).
As a relatively simplistic example, imagine that one generation (B) begins having its children
on average at age 22, and that those offspring (generation C) begin having their children on
average at age 28. Assume that each generation has two children two years apart, and that the
second child goes to university at age 20. When the second child of generation B parents
reaches university age, the parents will be aged 44. When the second child of generation C
parents reaches university age, the parents will be aged 50. Under this scenario, and assuming
a retirement age of 65 years, generation B parents will have, on average, around 21 years in
which to see their last child through university and concentrate on their own superannuation
provision before retirement. Generation C parents will have around 15 years. Any further delay
in the timing of childbearing or reduction in age at retirement would see the period available
for savings decrease.
The analysis may be further complicated by the age at which generation B’s own parents
(generation A) had its children. Until the mid-twentieth Century it was uncommon for retired
people to have their own parents still living; today, the ‘sandwich generation’, wherein older
cohorts have both dependent offspring and parents, is increasingly common (Young 1990).
As implied, such an analysis will also be complicated by inter-generational changes in the
proportion of life spent in the labour force, compared with changes in life expectancy.
Currently, people are living longer than ever before, but, at least for males, spending a shorter
period in the formal workforce. Ruzicka (1986 p. 22) estimated that the average male aged
15 years in 1933 would spend approximately 44 years or 83 per cent of his life in the labour
force; over the 1940s and 1950s this increased slightly to 84 per cent, but by 1981 the
proportion had declined to 72 per cent (41 years), despite an increase in life expectancy of
more than four years. These data have not been updated, but a comparison of age-specific
labour force employment rates for males in 1947 and 1996 against a further increase in life
expectancy at age 60 of 4.6 years over the same period, indicates substantial further
compression. By 1996 only 47 per cent of males had entered the labour force by age 15–19,
compared with 80 per cent in 1947. Only 51 per cent were still in the labour force at age
60–64, compared with 80 per cent in 1947.
Accordingly, the relative ability of the population to fully provide for its own education,
health care, old age, and/or to care for others, may have a significant cohort-level dimension.
Some cohorts may experience more or less difficulty than others, due to underlying
demographic forces of which they themselves played only a small part. Failure to understand
these constraints may see younger cohorts reduce their fertility still further, as they seek to
maximise (or protect) their own material wellbeing.
33
The policy-maker's guide to population ageing: key concepts and issues
Analysis of the phenomenon of demographic compression is extremely complex, and as yet
relatively undeveloped. In the interim, it is increasingly important that policy makers and
analysts think intergenerationally as well as longitudinally.
34
Demographic compression
10 Age structure and the welfare state—a ‘social’ or
‘demographic’ contract?
The Australian welfare state was officially established in 1943. Since its inception it has been
based on the notion of the social contract, an implicit agreement between the state and the
populace under which the economically active population is taxed, and these taxes are
redistributed by the state as income support and services to the eligible dependent
population (typically economically-inactive persons meeting specific criteria). Importantly, it is
a pay-as-you-go form of welfare state, where all benefits are funded from current taxation.
There are no vested funds for individual contributors.
Over the past three decades, a growing number of changes to Australia’s welfare state have
been introduced. Uppermost among these is the increasingly strong state encouragement for
and requirement of self-provision for post-compulsory education, health care, and
superannuation. The emerging situation means that the young and middle-aged are
simultaneously required to provide for their own education, health care and superannuation.
They also pay taxation to support the currently old and those nearing retirement who do not
now have time to self-provide. These changes are heralding a decisive shift from an internally
coherent, universal, tax-based, flat-rate system (Castles 1994) to a more mixed or segmented
self-funded, multi-tiered system, such as is found in the United States (Heidenheimer et al.
1990). As a result, the situation contains a serious challenge to the social contract, the
legitimacy of which depends on equity and continuity of access between generations
(Thomson 1992a, b).
Understanding how this tension is developing can be assisted by consideration of the changing
demography. When the Australian welfare state came into being, the population was
structurally young (approximately 5 per cent aged 65+ years). From the late 1930s it grew even
younger, as fertility increased (it had been slowly declining since the 1870s) and gathered
momentum with the baby boom. This trend continued for the next two and a half decades, until
the peak of the baby boom in 1961. Thereafter, as fertility again fell, the long-term trend
towards structural ageing resumed.
These dynamics meant that Australia’s welfare state (like the welfare states of much of the
developed world) was therefore created during a period in which a particular age structure
was extant—youthful and juvenescent. Ever-increasing numbers of young people were
heading towards the labour force (or primary tax-base), and, while youth dependency was
high, aged dependency, which typically costs two to four times as much (Borowski & Hugo
1996 p. 49), was low. From such a perspective, neither the manifestation nor implications of
excessive structural and numerical ageing could easily have been foreseen. This remained true
during the 1970s, when significant changes to welfare provision were enacted (see, for
example, the Borrie Report). Although falling, fertility was still relatively high (the TFR
averaging 2.4 across the decade), childbearing relatively universal, and there had been little
improvement in life expectancy at older ages. At the time it seemed feasible to continue, even
strengthen, welfare provision. Now, it is time to reflect that the development of the welfare
35
The policy-maker’s guide to population ageing: key concepts and issues
state may not have depended upon the social contract as much as upon a youthful age
structure, a demographic contract (Thomson 1992a, b).
Moreover, it has been postulated that continuity of the pay-as-you-go form of welfare state
may actually accelerate structural ageing, via a ‘taxation-fertility’ spiral (Weaver 1986, p. 311).
In what is probably a worst case scenario, it is argued that as the demand for Age-Pension and
other elder-specific services (for example, expensive health procedures) increases as a result of
numerical ageing, governments will have little alternative than to cut benefits and services, or
access to these, or dramatically increase taxation levels. If the latter became the chosen option,
higher taxation levels would conceivably see women undertake still higher levels of labour
force participation than at present, as they sought to maintain current familial living standards.
Such a situation would be expected to have a further depressing effect on fertility, and its
outcome, a further increase in structural ageing. As structural ageing increased further, taxation
would need to be further increased, creating a continuing downward pressure on fertility.
The results of such a scenario would not only be catastrophic for the welfare state; they would
also have significant political ramifications. Seemingly, more likely scenarios will include a
state-encouraged shift to later retirement (Bishop, in Access Economics 2001),11 and additional
but incremental changes in access to benefits along the lines already being implemented, such
as the currently occurring changes in the age of eligibility for Age Pension for females. Panels
A and B of Figure 14 give an indication of the impact of increasing the age of eligibility for
females from 60 to 65 years incrementally over a decade. The data assume no change to
current age-specific rates (that is, uptake). The reduction in the component of change due to
the changes in age structure (proportions at each age), and the increased numbers of elderly,
is clear (see section 13 for standardisation methodology. The fiscal savings could be readily
computed from these data).
Figure 14: Projected changes in numbers of females receiving Age Pension under different
eligibility criteria
Panel A: Projected numerical change over 1997 (assuming no change in current
age-specific rates), females aged 60+ years
2,000,000
1,750,000
Number
1,500,000
1,250,000
1,000,000
750,000
500,000
250,000
Year
Number in 1997
36
Effect of population size and age-structure
2020
2018
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
0
Age structure and the welfare state—a ’social‘ or ‘demographic’ contract?
Panel B: Projected numerical change over 1997 (assuming no change in current
age-specific rates, but applying new age-eligibility conditions), females
2,000,000
1,750,000
1,500,000
Number
1,250,000
1,000,000
750,000
500,000
250,000
2020
2018
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
0
Year
Number in 1997
Effect of population size and age-structure
Source: Compiled from Jackson 1999 (Department of Social Security unpublished data and ABS Population Projections 1998, Series II)
Importantly, changes such as these should be clearly related to the context of improved life
expectancy. The issue of Age Pension is illustrative. Prior to the establishment of the welfare
state in 1943, discrete benefits such as the Age Pension (1909) had been introduced. At the
time, life expectancy at birth (55 years for males and 59 years for females) was lower than the
age of eligibility (65 years for males and 60 for females). For those who reached the age of
eligibility (in the 1970s, when the Borrie Report was received), a further 9.4 years (on average)
could be expected for males; a further 16 years for females (see section 3 on life expectancy).
Currently, a male reaching age 65 can expect to live on average a further 14.6 years; a female
reaching age 60, a further 24 years. There are many indications that this increase will continue.
Trends such as these, positive though they are, necessitate what must be understandable
changes in eligibility criteria.
37
The policy-maker’s guide to population ageing: key concepts and issues
38
Policy and population ageing
11 Policy and population ageing
The relationship between policy and demographic change in general, and population ageing
in particular, is easier to understand if the term policy itself is first paid some analytical
attention. Demographers make useful distinctions between ‘explicit’, ‘implicit’, ‘direct’ and
‘indirect’ policy (Lucas 1994). Also in the demographic lexicon are ‘unintended’ and ‘net’ policy
effects.
• Explicit policies are those where the objective is formally stated, written down, acted upon
by a specific set of bureaucrats, and so on. A classic example would be Australia’s migration
policy.
• Implicit policies are those that are not formally stated, written down, necessarily acted
upon by a specific set of bureaucrats, and so on. They do, however, typically have intended
effects. An example would be the sale of contraceptive devices. This is a policy which
implicitly encourages fertility limitation, but which is not made explicit in a country like
Australia because of its near-universal acceptance.
• Direct policies are those that are developed with the objective of directly altering the
phenomenon or situation in mind. An example would be raising the age of eligibility for
Age Pension in order to reduce, at least in the short-term, the cost of Age Pension.
• Indirect policies are those that are developed with the objective of altering the
phenomenon or situation in mind via an indirect mechanism. An example would be the
payment of child allowance in the hope of raising fertility (or reducing structural ageing).
• Unintended policy effects are those that arise as an unintended consequence of the above.
An example would be a further fall in fertility and an increase in structural ageing as a
result of the introduction of user-pays fees for education (such as Australia’s Higher
Education Contribution Scheme (HECS). The accumulation of large education-related debts
could be expected to cause individuals and couples to delay family formation and/or to
have less children than they may have otherwise wished (Jackson, forthcoming).
Self-provision for health care and retirement may have similar effects.
• Net policy effects are similar to unintended effects, but are the manifestation of two or
more policies that contain conflicting or mutually compensating elements (Johansson 1991).
An example would be a reduction in fertility and an increase in structural ageing if there
was a reduction in financial support for child care (policy objective: fiscal saving) at the
same time as there was an increase in the number of women working to pay off their HECS
debt (policy objective: fiscal saving).
When disaggregated in this manner, it can be understood how policies that are developed to
respond to, for example, numerical ageing (for example, self-provision for health care and
retirement), or even apparently unrelated factors (fiscal savings in education; industrial and
labour market policy) may unintentionally exacerbate structural ageing (Chesnais 1996; Esping
Anderson 1996; McDonald 1997, 1999, 2000). Similarly, other policies, such as those facilitating
39
The policy-maker’s guide to population ageing: key concepts and issues
the casualisation of the labour force, may inadvertently stimulate fertility, thereby adding to the
dependency ratio. For policymakers, who often work in terms of explicit and/or direct policy
effects, the following is a memorable quote:
If policy is acknowledged to exist in diverse, and even invisible incentive-like forms (which
are not necessarily written down, or enforced by a specific set of bureaucrats, or even
related to the consciously articulated thoughts of a governing elite), one can begin to
coherently argue that, ‘theoretically’, policy is always efficiently enforced, and is always an
active determinant of fertility, indeed the most important one in virtually all cases
(Johansson 1991, p. 383).
In short, it is essential to understand that policies that have no demographic objectives often
have demographic effects, yet also that it is almost impossible to determine precisely which
factor delivered (or did not deliver) which effect. Some observers believe that the impact of
indirect political action on fertility (for example) is much stronger than that of policies
designed explicitly to affect fertility (Höhn 1986, 1987). This is especially so in respect of
efforts to increase fertility (pro-natal policies).
Much literature pertaining to the vexed question of how to bring about an increase in fertility
(and/or whether this is desirable) exists, and is beyond the scope and interests of this
publication to review in detail. Indeed, before venturing into that sphere it would be necessary
to review explanations for low fertility as such, a huge task that this paper is purposely not
attempting (for an excellent overview see McDonald 2000. See also Birrell & Rapson 1998 for an
implicit explanation related to declining levels of partnering).
However, it can be recorded that the effects of explicit and/or direct pro-natal policies have
typically been found to be nil or negligible (Demeny 1986, p. 350; Höhn 1987). The three
main exceptions: Germany’s rise in the birth rate of the 1930s as a result of eugenic policies;
Romania’s increase following a ban on abortion in 1966; and Singapore’s early 1990s increase
as a result of giving tax exemptions for higher numbers of children to the higher
socioeconomic strata. These were all temporary effects only, and are not examples likely to be
pursued by Australian policy makers. On the other hand, since the issue is likely to receive
much more attention in the near future, a brief review of tried and proposed measures is given
below.
• Höhn (1986, 1987) and Hugo (2000) provide an overview of measures attempted in several
European countries, many of which have also been implemented in Australia at various
times. Pro-natal policies include: child allowances, birth grants and loans, income tax relief
and incentives, income splitting, paid and unpaid child-rearing leave with re-employment
guarantees, childcare facilities, mutual responsibilities of families and societies (children not
seen as a private good only), access to subsidised housing, monthly salaries at the birth of a
second or subsequent child, free education, restricted sale of contraceptives (mainly Eastern
European countries), and (in Romania only) a taxation on childlessness. Generally,
expenditure for the more directly subsidised measures is considerable, and the effects
short-lived. Greater success appears to come from the more social measures that reduce
role incompatibility (between family and work) and opportunity costs (foregone earnings
40
Policy and population ageing
and seniority, superannuation contributions, risk of re-employment). In other words,
policies that alter the environment in which people make decisions about having children
are likely to be the most successful.
• In Sweden, for example, not only is paid parental leave institutionalised, but it is mandatory
that one month of that leave be taken by the father (Chesnais 1996 p. 733). These measures
reduce the immediate opportunity costs of childbearing and rearing, and contribute to
gender equity. According to many commentators, empowerment of women ensures against
a very low birthrate.
• Reflecting these arguments, McDonald (1997, 2000; see also Chesnais 1996; and EspingAnderson 1996) argues for more ‘family-friendly’ workplaces. The very low levels of fertility
experienced in developed countries today are largely ascribed to an incoherence between
the levels of gender equity applying in different social institutions, such as the family and
the market place. Where gender equity in these institutions is low, or differs markedly
between institutions, fertility is very low (that is, considerably below the TFR required for
generational replacement); where it is higher, as judged, for example, in Sweden, fertility is
higher (around or closer to replacement level). As McDonald (1997, p. 1) explains, when
women have access to the same educational and employment opportunities as men, but
these opportunities are severely curtailed by having children, then women will restrict the
number of children that they have. Inflexible workplace arrangements that penalise, rather
than encourage, those who have children, are particularly correlated with low fertility. It is
at this juncture that policy interventions might most usefully be directed.
• Demeny (1986) proposes formal incorporation of the (nuclear) family. Revenues, however
acquired (and presumably taxation liabilities), would accrue to the corporation, becoming
equally vested in spouses. This would enhance the economic security of women and
provide for greater choice in matters pertaining to labour force participation, household
production, and child rearing. Problems would be experienced in defining the family unit,
while the underlying assumption of equal sharing and reciprocity within the family could
not be taken uncritically.
• Demeny (1986, 1987) also proposes linking old-age economic security with prior fertility
behaviour. The aged, in aggregate, have raised the subsequent generation of taxpayers who
make the system viable (whether for pensions or investment returns, funds come primarily
from the productive efforts of the current generation of workers). Individual demographic
contributions to the aggregate should be recognised through differential access to the
resources eventually generated. Women who have taken time out of the labour force to
raise the future taxpayers are especially disadvantaged in situations where self-provision for
retirement is required. (However, so too are those who have experienced long-term
unemployment and who also may not have had children.)
41
The policy-maker’s guide to population ageing: key concepts and issues
42
Population projections
12 Population projections
Most discussions concerning population ageing are based on population projections. It is
common to see criticisms of these projections. Most typically the criticism will include the term
predictions. Population projections are not predictions. They are based on clearly specified
assumptions about the three demographic factors that together cause population change:
births, deaths and migration. Past and present levels of these factors are used to develop
several sets of assumptions (variants). For example, a combination of higher fertility, lower
mortality, and higher net migration than is currently extant usually comprises the high variant
assumption. Similarly a combination of lower fertility, higher mortality and lower net migration
than is currently extant usually comprises the low variant. The various assumptions used by
the ABS are always published along with the projections themselves (see ABS 3222.0).
Projections are calculated using the cohort component method:
P1 = P0 + B - D + NM
Where
P1 = the ‘new’ population
P0 = population at the present time
B = Births
D = Deaths
NM = net migration (the difference between in migration and out migration).
The analyst begins with a census-derived base population by sex and single year of age (such
as appears graphically in a population pyramid). The birth rate assumption is applied to the
number of women at each single reproductive age (15–49 years) and the resulting projected
number of births is added to the base of the population age structure. The death rate
assumption for each single age and sex group is then applied to the resulting age structure.
Finally the migration assumption for each single age and sex group is applied, the resulting
numbers being either added to, or subtracted from, the numbers at each age. The population
is then ‘aged’ by one year to become the new base population, and the process is repeated for
each successive year. The calculations are made separately for each statistical local area, with
different fertility, mortality and migration assumptions being used for urban and rural areas.
These data are then aggregated to provide total and State/Territory level data.
The resulting projections, which derive both age structures, and total numbers, indicate what
the outcome will be if (and only if) the specified assumptions have been met. As such, they
provide a useful benchmark against which actual trends can be plotted.
Currently, the ABS produces 24 sets of projections; typically only three are published: Series Ia
(the high outcome variant), Series IIa (the medium outcome variant), and Series IIIa (the low
outcome variant), sometimes referred to as the ‘best case’, ‘medium case’ and ‘worst case’
scenarios. Conventionally, where data from only one set of projections are presented, they
reflect the medium variant. This is especially so with international data comparisons.
43
The policy-maker’s guide to population ageing: key concepts and issues
Because birth and death rates typically change quite slowly, and international migration into an
island nation such as Australia is reasonably well controlled and monitored, projections for the
immediate years and decades can be considered highly reliable approximations. However, it is
important to note that all measures of migration are somewhat less reliable than births and
deaths data, which are derived from Vital Registrations. In particular, internal migration data,
which are based on Medicare ‘change of address’ registrations, are subject to many limitations.
Because of these shortcomings, longer-range projections (to 2051 or longer), should always be
viewed as educated guesses.
The ABS issues a new set of projections every second year. They are of course based on
revised sets of assumptions that have taken account of demographic changes during the
previous two years.
One other type of population projections deserves a brief mention. These are intercensal
projections, which, as their name suggests, are short-term projections undertaken between
censuses, which are themselves usually undertaken every five years. A very similar process to
that described above is carried out, with the outcomes being revised after the following census.
44
Methodological implications—some useful techniques
13 Methodological implications—some useful techniques
The factors outlined in this paper have a number of methodological implications for policy
makers and analysts. Among these is the need to control for compositional changes in the
phenomenon being studied. For example, if the proportion of a population receiving Age
Pension increased over time, we would want to know what the proportion would have been
if the age structure had not changed. This can be established via a simple technique called
direct standardisation. Using a slightly more refined technique called decomposition
analysis, we can also show (a) what proportion of that increase was due to an increase in the
numbers of elderly, and/or (b) what proportion was due to an increase in uptake (those
applying for Age Pension who previously would not have). The former (a) would reflect the
effect of population ageing (that is, it would have a demographic explanation) while the latter
(b) would reflect a true increase (that is, it would have a social or economic explanation).
In technical terms, the problem is defined in the following way. Any summary measure (for
example, the percentage of a population receiving an income support payment) is the product
of at least two things. These are: (i) the underlying level or incidence of the phenomenon of
interest, and (ii) the composition of the population for which the calculation is being made;
that is, the extent to which the population of interest is concentrated in the compositional
categories where the phenomenon of interest is likely to occur (for example, age group, sex,
marital status group, educational or employment group). If the effects of (ii) are not controlled,
any ratio-type measure used to make comparisons either within or between populations, at
either a single point in time or over time, is at risk of yielding distorted comparisons
(Carmichael 1995).
Standardisation: With simple (direct) standardisation, the age-specific (or category-specific)
measures for one population are applied to the age structure (or category structure) of another
population (the standard population), and then summed. The algorithm is:
Ms(i) = c mi(c).ps(c)
Where:
Ms(i) = the summary measure for population i standardised to the composition of
population s
c = the compositional categories for the variable(s) being standardised (age, age-sex
category etc.)
mi(c) = the specific measure equivalent to M(i) for compositional category c for
population I
ps(c) = the proportion of the standard population s in compositional category c
Interpretation of these results proceeds by comparing the summary measure for the
standardised population with either its own non-standardised equivalent, or with the measure
for the standard population. Interpretation rests on one important axiom—that standardised
measures are hypothetical. That is to say, the resulting values are values we would expect the
summary measure in question to take on if it had the composition of the standard population.
45
The policy-maker’s guide to population ageing: key concepts and issues
Importantly, the standard population must match precisely the denominator for the summary
measure. That is to say, if the summary measure pertains to the proportion of the population
aged 65+ years receiving Age Pension, the standard population must cover the exact same age
groups.
Decomposition: Two-way decomposition is a refined form of standardisation that splits the
differences between two summary measures into components that are attributable to two
phenomena, for example as above, to changes in age structure and changes in uptake. The
algorithm for component analysis (Carmichael 1995 p. 51) is:
Csm = 0.5[M(1) – Ms1(2) + Ms2(1) – M(2)]
Cc
=
0.5[M(1) - Ms2(1) + Ms1(2) – M(2)]
Where:
Csm
=
Component due to differences in underlying characteristics
Cc
=
Component due to differences in population composition
M(1)
=
Summary measure 1, relates to population 1
M(2)
=
Summary measure 2, relates to population2
Ms1(2)
=
Summary measure 2 directly standardised to population 1
Ms1(2) =
Summary measure 1 directly standardised to population 2
This algorithm standardises the summary measures for each population against the age
composition of the other, deriving alternative expressions for Csm and Cc. The two values are
then summed and averaged. Interpretation then proceeds in a manner similar to that for direct
standardisation, only in the case of component analysis it is the sign (+ or -) on each
component that is important, and how this sign compares with that on the overall differences
between the two original (unstandardised) summary measures (Carmichael 1995). If the sign
on the component is the same as that on the overall difference, that component helped
produce the overall difference. If the sign on the component is opposite to that on the overall
difference, that component has partially offset, or moderated the overall difference (that is,
made it less substantial than it otherwise would have been).
Figure 15 shows the effect of decomposition analysis on the proportion of the Australian male
population receiving the Disability Support Pension (DSP) between 1971 and 1997. The
substantial growth in numbers receiving this pension has, in the past, been superficially
attributed to population ageing. However, as Figure 15 shows, the effect of changes in the age
structure have been negligible. For most of the period shown, population ageing (or more
correctly, changes in cohort size (see section 5) had an offsetting effect, becoming additive
only in 1997, and then only fractionally. This finding is explained by the fact that the first of the
baby boomers have only just passed age 50 and entered the key DSP age group. Thus, the
growth in the numbers receiving DSP has been real in the sense that it cannot be attributed to
population ageing.
46
Methodological implications—some useful techniques
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
-0.5
1971
Percentage Point Change over 1971
Figure 15: Components of change in disability support pension (percentage point change over
1971), males 1971–97
Component due to age structure
Component due to change in underlying rate
Observed percentage point change
Source: Compiled from Jackson 1999 (Department of Social Security Unpublished Data, and ABS Population Projections (1998)
Series II)
Notes: The age structure effect is barely visible, showing just below the line denoting zero growth, over some of the years 1979–1994
Similarly, decomposition analysis of several other Commonwealth income support categories
identifies that population ageing has as yet had very little effect on any payment category
other than Age Pension, and then only for females (Whiteford & Jackson 1998; Jackson 1999,
Figure 3.4, Figure 3.5 and Figure 3.6). By contrast, in a manner almost identical to that for the
DSP, population ageing, or more accurately, changes in cohort size, has partially contained the
demand for, or growth in, spending on unemployment related allowances.
These findings and their technical underpinnings are very important for policy makers, advisers
and analysts working in such areas as income support and services, because if changes in the
numbers (or proportions) of the population receiving certain payments and benefits are
erroneously attributed to population ageing, the resulting policy interventions may fail.
Moreover, failure to specify appropriately the ‘problem’ can also be highly detrimental to
those people who comprise the affected groups. For example, since the early 1980s changes in
cohort size have had a small additive effect on the numbers of females receiving the
Supporting Parent/Sole Parent Pension, (SPP) the reason being that the age group with the
highest incidence of SPP receipt (30–39 year olds) has also been the largest age group in the
population because it contains the peak baby boomers. In fact the age-effect is very small (in
the late 1990s accounting for less than 4 per cent of growth in numbers since 1975), but it
serves as a useful illustration. Not all growth in SPP numbers is due to an increase in uptake;
nor is it due to population ageing. Rather, at least some of it is due to changes in cohort size.
Finally, another factor demanding the use of standardisation and/or decomposition analysis is
change in the family and the household. Among other forces, population ageing is a significant
driver of the widely reported decline in the couple with children (or two-parent) household
47
The policy-maker’s guide to population ageing: key concepts and issues
and a concomitant increase in couple only (no children) and sole person households. Agestandardisation of such data readily identifies what might be termed a cascading effect. A
quantifiable proportion of the decline in the couple with children household is simply due to
the shift to later family formation and thus later entry into this household type, while there is a
corresponding increase at these ages in the proportions residing in couple only families
(Jackson & Pool 1996, pp. 163–64). The trend is further compounded by smaller average
family sizes than in the past, which mean that the ‘empty nest’ phase is reached earlier. This
results in reduced proportions in couple with children households at the middle to early old
ages, and again, an increase in couple only households at these ages. Finally, longer life
expectancy is further extending the period spent in the couple only household, while the
higher life expectancy of females than males, coupled with numerical ageing, is causing a
similar increase in the sole person household at older ages.
The overall effect is a reduction in the proportion of the total population residing in a couple
with children household, against an overall increase in the number of households, and a
decline in number of persons per household. These trends, which the momentum of ageing
contained within the age structure ensures will now accelerate, are often attributed (by the
media) to the increase in sole parenting and/or the number of elderly living alone. Certainly
the latter are contributing factors, but the changes fall far short of accounting for the decline in
the couple-with-children household as such. Age-standardised analyses would contribute
substantially to the debate.
48
Endnotes
Endnotes
1
It is difficult to define precisely the beginning and end of the baby boom (it differs slightly in each developed
country), but the Australian Bureau of Statistics recognises the period 1946–65 because in 1965 the TFR had
fallen below its 1946 level of 2.98.
2
The proportion of the population aged 15–64 years is projected to peak in 2009 at around 68.1 percent and fall
to under 60 per cent by 2051 (ABS Series IIa).
3
It should be noted that although these levels have risen in recent years, they are lower than those experienced
early last century.
4
The infant mortality rate and migration are also involved, but for the purposes of this discussion the birth rate
will suffice.
5
A youth deficit is defined as occurring when the proportion of the population aged 15–24 years falls below
15 per cent. In 1980, no countries had recorded this phenomenon. By 1985, it was apparent in seven
countries, and currently (2001) it can be observed in 54 countries with many others close behind.
6
Australia’s current doubling time is approximately 63 years. The period is increasing rapidly as the rate of
growth falls.
7
It is here that the distinction between structural and numerical ageing is again useful. The declining number of
births is the cause of structural ageing; the increasing number of deaths, the result of numerical ageing.
8
Replacement migration essentially means the replacement of babies with migrants.
9
However, because of the onset of natural decline from around 2030–2040, this would see the population in
2098 only 5.7 million greater than at present.
10
With the exception of 1986, the Australian census has historically collected ‘ethnic’ data by ‘country of birth’
(for example, New Zealand). These data do not determine the ethnicity of migrants, which is related to
cultural affiliation. An approximation of an ethnic group age structure could possibly be achieved by
combining these data with data for Australian-born people with parents born in that birthplace, but the result
would still not reflect actual ethnic or cultural affiliation.
11
Such a shift is likely to be politically acceptable, given that more than half of recent retirees would have
preferred to continue their employment for longer (ABS 1998).
49
The policy-maker’s guide to population ageing: key concepts and issues
50
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Useful web sites
www.abs.gov.au
Australian Bureau of Statistics’—contains downloadable demographic and socioeconomic data, including
information on concepts, projections and so on.
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The policy-maker’s guide to population ageing: key concepts and issues
www.stats.govt.nz
Statistics New Zealand —contains downloadable demographic and socioeconomic data for New
Zealand. A significant feature of the New Zealand data is the attention paid to ethnic differentials.
www.health.gov.au
Australian Department of Health and Aged Care—with a link to the National Strategy for an ageing
Australia.
www.aifs.org.au/
Australian Institute of Family Studies—with links to databases and publications.
www.demography.anu.edu.au/VirtualLibrary/
Australian National University demography program—has links to hundreds of leading information
facilities of value and/or significance to researchers in the field of demography.
www.immi.gov.au/
Australian Department of Immigration and Multicultural Affairs.
www.gisca.adelaide.edu.au/apa/
Australian Population Association—includes a Population Facts booklet and downloadable, related
information.
www.census.gov/ipc/www/idbnew.html
United States. Bureau of the Census International Data Base— includes a computerised data bank
containing statistical tables of demographic and socioeconomic data for 227 countries and regions; can
generate tables and pyramids for the present and future; and has a dynamic option via which projected
changes in these population age structures can be observed for the next fifty years.
www.census.gov/population/www/socdemo/age.html
United States. Bureau of the Census—contains demographic information and data on population ageing
in the United States.
www.ssa.gov/OACT/TRSUM/trsummary.html
United States. Social Security System—contains reports for 1999 of special interest in relation to
population ageing.
www.nih.gov/nia/bsr/bsrdda.htm
Demographic research on population ageing by the United Nations—includes links to many other
age-related web sites.
www.unece.org/ead/age
United Nations Economic Commission for Europe—contains details of work by the United Nations on
population ageing.
www.unece.org/ead/pau/age/a_h_6.htm
United Nations—contains a number of other United Nations’ resources.
www.undp.org/popin/wdtrends/fer/fercht.htm
United Nations—includes references on fertility data for a large number of countries and regions.
56
Bibliography
www.popplanet.org/
National Council on Science and the Environment— access to an wide range of country briefing books.
Abstracts allow quick identification of the resources that are most useful.
www.sosig.ac.uk/roads/subject-listing/World/natstat.html
Social Science—information gateway to a huge range of sites, including for the 1970 British cohort
study.
www.rpieurope.org/download/ageing.html
Economic consequences of population ageing.
www.un.org/Pubs/CyberSchoolBus/special/globo/glotrend/index.html
Global trends in population, health, economic factors etc.
www.un.org/Pubs/CyberSchoolBus/infonation/e_infonation.htm
Easy-to-use, two-step database enabling comparison of statistical information for United Nations’
countries.
www.sosig.ac.uk/roads/subject-listing/World/demog.html
Set of links to selected, evaluated and annotated Internet resources relevant to demography.
bubl.ac.uk/link/w/worldpopulation.htm
Contains approximately 24 000 time-series for 196 countries and geographical areas covering
population, exchange rates, fund accounts, international liquidity, international banking, money and
banking, interest rates, prices, wages, production and employment, international transactions,
government finance, national accounts. Some sites require authorisation from the Data Archive before
access is permitted.
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