Number 13: The policy-maker's guide to population ageing: Key concepts and issues

This report was published by the former Department of Families, Community Services (FaCS).

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.
  • 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.
  • 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.

[ Return to Top   Return to Section ]

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)

Figure 1: The demographic transition (classic or western model)

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–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.
  • Numerical ageingon 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 micro-economic 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).

Figure 2:  Age–sex structure of the Australian  population, 1976, 1996 and 2016

Figure 2:  Age–sex structure of the Australian  population, 1976, 1996 and 2016

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).

[ Return to Top   Return to Section ]

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:

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.

Figure 3:  Median ages of mothers (all births), Australia 1921–98

Figure 3:  Median ages of mothers (all births), Australia 1921–98


Source: 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.

Figure 4: Total fertility rate 1921–99, and completed fertility rate for cohorts born 1905–60 lagged by 30 years, Australia

Figure 4: Total fertility rate 1921–99, and completed fertility rate for cohorts born 1905–60 lagged by 30 years, Australia

Source: Compiled by the author. ABS Catalogue 3301.0, Various Years
Notes: The latest completed fertility data available are for the cohort born 1950; data for cohorts born 1950-60 have beenestimated. 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

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).

[ Return to Top   Return to Section ]

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 at birth (eo0 ). 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 (eo60) 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.

[ Return to Top   Return to Section ]

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 oldwhen  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 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+

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).

Figure 5:  Youth (0–19 years), aged (65+ years) and total  (0–19 and 65+ years) dependency ratios, Australia 1971–2051

Figure 5:  Youth (0–19 years), aged (65+ years) and total  (0–19 and 65+ years) dependency ratios, Australia 1971–2051

Source: Compiled by the author.
1971-1998: ABS Population Estimates; 1998-2051: ABS 2000, Catalogue 3222.0, Series IIa

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 1.7 2.4
2005 1.5 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.

[ Return to Top   Return to Section ]

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.

Figure 6: Total fertility rates and cohort size, Australia, 20th Century

Figure 6: Total fertility rates and cohort size, Australia, 20th Century


Source: Compiled by the author
ABS Australian Demographic Trends 1997 Appendix 16; ABS Births, various years
Notes: 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):

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.

Figure 7:  Projected increase in populations aged 55–64, 65–74 and 75+ years, Australia

Figure 7:  Projected increase in populations aged 55–64, 65–74 and 75+ years, Australia

Source: Compiled by the author
ABS 2000, Catalogue 3222.0, Series IIa

Each Australian cohort is also differentiated ethnically, with high  proportions (around40 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 inter- cohort 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).

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

Figure 8:  Labour market entry-exit ratios (18–24:55–64 years), Australia 1971–2051

Source: Compiled by the author
1971–98 ABS Population Estimates; 1997–2051: ABS 2000, Catalogue 3222.0, Series IIa

[ Return to Top   Return to Section ]

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.6In 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.

Figure 9: Births and deaths, Australia, 20th Century, and projected

Figure 9: Births and deaths, Australia, 20th Century, and projected

Source: 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’.

[ Return to Top   Return to Section ]

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:

  1. maintenance of the size of the total population;
  2. maintenance of the size of the working-age population; and
  3. 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
Scenario   I II III IV V
Current size 2000 Medium variation migration Projected size 2050 medium variant* Constant total population size Constant age group 15-64 years Constant age group 64/65+ years
Country/region Thousands (per annum)
Germany 82,220 204 73,303 344 487 3,630
Italy 57,298 6 41,197 251 372 2,268
Japan 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:     United  Nations 2000, Tables  1, IV.14, IV.19, V.22,
Notes:      * Includes migration at the medium variant  assumption

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

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).

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 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.

[ Return to Top   Return to Section ]

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

Figure 10: Age-sex structures of the Aboriginal and Torres Strait Islander and total  Australian populations, 1996

Source: Jackson 1999, Figure 2.2

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.

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.

Figure 11: Percentage of each age group born overseas, by region of birth,  1998–99

Figure 11: Percentage of each age group born overseas, by region of birth,  1998–99

Source: Compiled by the author
ABS Catalogue 3412.0, 1998-99, Table 6.3
Notes: *Asia = North-East, South East, Southern, Middle East/North Africa

Figure 12: Projected percentages aged 65+ years, selected States and Territories

Figure 12: Projected percentages aged 65+ years, selected States and Territories

Source: Compiled by the author
ABS Population Projections 1997-2051, Catalogue 3222.0, Series IIa

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

Figure 13: Projected rate of natural increase and decline (per 1 000 persons), by State and Territory

Source: Compiled by the author
ABS Population Projections 1997-2051, Catalogue 3222.0, Series Ia

[ Return to Top   Return to Section ]

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.

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.

[ Return to Top   Return to Section ]

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 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

Figure 14: Projected changes in numbers of females receiving Age Pension under different eligibility criteria

Figure 14: Projected changes in numbers of females receiving Age Pension under different eligibility criteria

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.

[ Return to Top   Return to Section ]

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 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 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 Esping- Anderson  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.)

[ Return to Top   Return to Section ]

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.

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.

[ Return to Top   Return to Section ]

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:

Μs(i)   =   Σc mi(c).ps(c)


Μs(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.

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)]


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.

Figure 15: Components of change in disability  support pension (percentage point  change over 1971), males 1971–97

Figure 15: Components of change in disability  support pension (percentage point  change over 1971), males 1971–97

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 and a concomitant increase in couple only  (no children) and sole  person  households. Age- standardisation 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.


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).

[ Return to Top   Return to Section ]


Abbasi-Shavazi, M. 1998, the fertility  patterns  of selected Australian immigrant groups, 1977–1991, unpublished Ph.D. thesis,  The Australian National  University, Canberra.

Abbasi-Shavazi, M. & McDonald,  P. 1997, Fertility and multiculturalism: immigrant fertility  in Australia 1977–1991,  paper  presented to the International Population Conference, Beijing.

Access  Economics.  2001, Population Ageing and The Economy, Melbourne, Access  Economics  Pty. Ltd. Alvarado,  J. & Creedy, J. 1997, Migration, Population Ageing and Social Expenditure in Australia, Commonwealth of Australia.

Australian Bureau of Statistics,  Australian Demographic Trends, Cat. no. 3102.0,  various  years.

Australian Bureau of Statistics,  Births Australia, Cat. no. 3301.0,  various  years.

Australian Bureau of Statistics,  Census Australia, various  years.

Australian Bureau of Statistics,  Deaths Australia, Cat. no. 3302.0,  various  years.

Australian Bureau of Statistics,  Experimental Projections of the Aboriginal and Torres Strait Islander Populations, Cat. nos. 3230.0 and 3231.0.

Australian Bureau of Statistics,  Migration Australia, Cat. no. 3412.0,  various  years

Australian Bureau of Statistics,  Population Projections 1997–2051, Cat. no. 3222.0.

Birrell,  B. & V. Rapson.  1998, A Not So Perfect Match. The Growing Male/Female Divide, 1986–1996, Melbourne: Centre  for Population and Urban Research, Monash University.

Bishop,  B. 1999, The National Strategy for an Ageing Australia, Independence and Self-provision Discussion Paper, Minister for Aged Care, Commonwealth of Australia

Bongaarts, J. 1999, ‘Fertility decline in the developed world:  where will  it end?’, American Economic Association Papers and Proceedings, May, pp. 256–260.

Borowski, A. & Hugo,  G. 1996, ‘Demographic trends  and policy implications’, chapter  2 in A. Borowski, S. Encel, and E. Ozanne  (eds)  Ageing and Social Policy in Australia, Cambridge University Press, Melbourne.

Burnely, I. 1996, Atlas of the Australian People 1996, Australian Government Publishing Service, Canberra.

Caldwell, J., Caldwell, P., & McDonald,  P. 1998, Challenges of changing age  structures, paper  presented to International Symposium on Population and Development Policies  in Low Fertility Countries, Kihasa, May 7 to 12.

Carmichael, G. 1995, ‘Comparison: standardisation and decomposition’, chapter  2 in G. Carmichael, Methods  of Demography, Course Text, The Australian National  University, Canberra.

Castles,  F. 1994, ‘The wage earner’s welfare state revisited: refurbishing the established model  of Australian social  protection, 1983–1993’,  Discussion Paper No. 39, Public  Policy  Program,  The Australian National  University, Canberra.

Chesnais, J-C. 1996, ‘Fertility,  family  and social  policy in contemporary Western  Europe’,  Population and
Development Review, 22(4),  pp. 729–739.

CIA (Central  Intelligence Agency) 1990 ‘The CIA on youth  deficits’, Population and Development Review,
16(4),  pp. 801–807.

Coale,  A. 1972, The Growth and Structure of Human Populations, Princeton  University Press,  Princeton, New Jersey.

Coale,  A. 1972b,  ‘How a population ages  or grows  younger’, chapter  3 in R. Freedman (ed.) Population: The Vital Revolution, Doubleday–Anchor, New York, pp. 47–58.

Cowgill, D. & Holmes,  L. 1970, ‘The demography of ageing’ in A. Hoffman (ed.)  The Daily Needs and
Interests of Older People, Charles  C. Thomas,  Springfield.

Davis, K., Bernstam, M.S., Ricardo-Campbell, R., (eds)  1986, ‘Below  replacement fertility  in industrial Societies. Causes, consequences, policies’, Population and Development Review, Supplement to Vol. 12, Cambridge University Press,  Cambridge.

De Beer,  J., Beets,  G., Bosman, E., & Willems, P. 1991, ‘Chronicle: trends  in population and family  in the low  countries’, in G. Beets,  R. Cliquet,  G. Dooghe, J. Gierveld, & J. de Jong (eds)  Population and Family in the Low Countries, Swets  and Zeitlinger, Amsterdam, pp. 22, 133–215.

Demeny, P. 1972, ‘Early fertility decline in Austria-Hungary: a lesson  in demographic transition’, in D. Glass & R. Revelle  (eds)  Population and Social Change, Edward  Arnold,  London.

Demeny, P. 1986, ‘Pronatalist  policies in low-fertility countries: patterns, performance, and prospects’, in K. Davis, M. Bernstam, & R. Ricardo-Campbell (eds)  ‘Below  Replacement Fertility in Industrial  Societies. Causes, Consequences, Policies’, Population and Development Review, Supplement to Vol. 12,
Cambridge University Press,  Cambridge.

Demeny, P. 1987, ‘Relinking fertility  behaviour and economic security in old age:  a pro-natalist reform’,
Population and Development Review, 13(1),  128–132.,  pp. 22, 128–132.

Easterlin,  R. 1988, Birth and Fortune. 2nd   Edition., University of Chicago  Press,  New York.

Esping-Anderson, G. 1996, ‘Welfare  states  without  work:  the impasse of labour  shedding and familialism in Continental European social  policy’, in G. Esping Anderson  (ed)  Welfare States in Transition: National Adaptations in Global Economies, Sage  Publications, London,  pp. 66–87.

Gardiner, G. & Bourke, E. 2000, ‘Indigenous populations, ‘mixed’  discourses and identities’, People and Place 8(2),  43–52.

Hagenaars, J. 1990, Categorical Longitudinal Data, Sage  Publications, Newbury Park.

Heer, D. 1986, ‘Immigration as a counter  to below-replacement fertility  in the United  States’,  in K. Davis, M. Bernstam, and R. Ricardo-Campbell (eds)  ‘Below  Replacement Fertility in Industrial  Societies. Causes, Consequences, Policies’, Population and Development Review, Supplement to Vol. 12, Cambridge University Press,  Cambridge pp. 262–269.

Heidenheimer, A.J., Heclo,  H., and Adams,  C.T. (1990)  Comparative Public Policy. The Politics of Social Choice in America, Europe and Japan (Third Edition),  New York, St. Martins Press.

Höhn, C. 1986, Demographische Wirkungen Politischen Handelns, Zeitschrift für Bevölkerungswissenschaft 12, 1, pp. 3–51, and 2, pp. 185–219.

Höhn, C. 1987, ‘Population policies in advanced societies: pronatalist and migration strategies’,European Journal of Population, 3, pp. 459–481.

Hugo,  G. 1998, ‘South Australia’s  ageing population and its increasingly multicultural nature’, paper presented to the annual general meeting of Multicultural Aged Care, Adelaide.

Hugo,  G. 1999, ‘Regional development through  immigration? The reality  behind the rhetoric’,  Research Paper 9, Commonwealth of Australia,  Parliamentary Library,  Canberra.

Hugo,  G. 2000, ‘Declining fertility  and policy intervention in Europe:  Some lessons for Australia?  Journal of Population Research, 17(2),  175–197.

Jackson, N. 1994, Youth unemployment and the ‘core family’.  population, policy and political economy, unpublished Masters  thesis,  University of Waikato.

Jackson, N. 1998a, Ethnic stratification in New Zealand. a total social  production perspective, unpublished Ph.D. thesis,  The Australian National  University, Canberra.

Jackson, N. 1998b,  ‘Demographic compression and its implications for familial  self-reliance’, Paper presented to the Australian Institute of Family Studies Conference, Melbourne, 25–27 November.

Jackson, N. 1999 ‘Understanding population ageing: a background’, Australian Social Policy (1),  pp. 203–224.

Jackson, N. (forthcoming) ‘The Higher  Education  Contribution Scheme. A HECS on ‘the family’?,  in G. Carmichael & D. Dharmalingham, (Eds.)Australia and New Zealand at the Millennium, proceedings of the Australian and New Zealand Population Association’s joint conference, Wellington.

Jackson, N. & Pool, I. 1996, ‘Will the real  New Zealand family  please stand  up? Substantive and methodological factors affecting  research and policy on families  and households’, Social Policy Journal of New Zealand, 6, pp. 148–176.

Johansson, S. 1991, ‘‘Implicit’  policy and fertility  during  development’, Population and Development Review, 17(3),  pp. 377–414.

Keyfitz, N. 1971, ‘On the momentum of population growth’, Demography, 8, pp. 71–80.

Kippen,  R. 1999, ‘A note on ageing, immigration and the birthrate’, People and Place, 7(2),  pp. 18–22. Kippen,  R. & McDonald,  P. 2000, ‘Australia’s  population in 2000: the way  we  were  and the ways  we might have  been’,  People and Place, 8(3),  pp. 10–17.

Le Bras, H. 1991, ‘Demographic impact  of post-war migration in selected OECD countries’, in Migration. The Demographic Aspects, OECD, Paris, pp. 15–26.

Lucas, D. 1994, ‘Population policies’, chapter  13 in D. Lucas and P. Meyer  (eds)  Beginning Population Studies, 2nd   Edition, The Australian National  University, Canberra.

Lutz, W. (ed.) 1994, The FuturePopulation of the World. What Can We Assume Today? London, Earthscan.

Lutz, W. (ed)  1996, The FuturePopulation of the World. What Can We Assume Today?, rev ed,  London, Earthscan.  ( (30–05–01)

Mackay, H. 1997, Generations: Baby Boomers, Their Parents and Their Children, Macmillan, Sydney.

McDonald,  P. 1997, ‘Gender  equity, social  institutions and the future of fertility’,  Research School of Social Sciences Working Papers in Demography No 69, The Australian National  University, Canberra.

McDonald,  P. 1998, ‘Contemporary fertility  patterns  in Australia:  First data  from the 1996 Census’,  People and Place, 6(1),  pp. 1–17.

McDonald,  P. 2000, ‘Low fertility  in Australia:  evidence, causes and policy responses’, People and Place, 8(2),  pp. 6–20.

McDonald,  P. & Kippen,  R. 1999, The impact of immigration on the ageing of Australiaspopulation, Department of Immigration and Multicultural Affairs, Canberra.

McDonald,  P. & Kippen,  R. 2000, Strategies for labour  supply in sixteen developed countries, 2000–2050, paper  presented to the 2000 Annual  Meeting  of the Population Association of America,  Los Angeles, California, 23–25 March.

McNicoll, G. 2000, ‘Reflections on ‘replacement migration’’, People and Place, 8(4),  pp.1–13.

McPherson,  M. 1992, Cohort vulnerability to lack  of support  in old age:  a theoretical and analytical exploration, unpublished Master of Social  Science Thesis,  Hamilton,  University of Waikato.

Merlo, R. and Rowland, D. 2000, ‘The prevalence of childlessness in Australia’,  People and Place, 8(2), pp. 21–32.

Petersen, G. 2000, ‘Gray dawn. The global aging  crisis’,  in F. Moulder  (ed.),  Social Problems of the Modern World, Wadsworth/Thomson, Australia,  pp. 126–134.

Pool, D. 1987, Implications of changes in the cohort/age structure  of the New Zealand population, paper  presented to the New Zealand Demographic Society,  Auckland, 10.July.

Pool, I. 1991, Te Iwi Maori. A New Zealand Population. Past, Present and Projected. Auckland University Press,  Auckland.

Ruzicka,  L. 1986, The Length of Working Life of Australian Males, 1933–1981, Monograph Series  No.15, Bureau of Labour Market Research, Australian Government Publishing Service,  Canberra.

Sceats,  J. 1992, Competing generations: the New Zealand family  meets  the free market,  Keynote Address,  National  Conference, Parent–Centres of New Zealand, Rotorua,  New Zealand.

Shryock, H. and Siegel, J. 1971, The Methods and Materials of Demography, U.S. Bureau of the Census, Washington  D.C.

Simon,  J. 1984, ‘Immigrants, taxes  and welfare in the United States’, Population and Development Review, 10(1)  pp. 55–69.
Stockwell, E.G. 1976, The Methods and materials of Demography, San Diego-London, Academic Press. Thomson,  D. 1992a,  Selfish Generations. The Ageing of New ZealandsWelfare State, Bridget  Williams Books,  Wellington.

Thomson,  D. 1992b,  ‘Welfare  states  and the problem of the common’, CIS Occasional  Papers 43, Social Welfare  Research Program,  New South Wales,  Centre  for Independent Studies,  Sydney.

Thompson, D. 1998, A World Without Welfare. New ZealandsColonial Experiment, Auckland University Press,  Bridget  Williams  Books,  Auckland.

United  Nations 2000, ‘Replacement Migration, Population Division,  Department of Economic  and Social Affairs, United  Nations Secretariat.

United  States Bureau of the Census  1999, ‘Report WP/98’, World Population Profile: 1998, U.S. Government Printing  Office, Washington, DC.

United  States Bureau of the Census,  International Database, (20-05-01).

Van de Kaa, D. 1998, ‘Postmodern fertility  preferences: from changing value  orientation to new behaviour’, Research School  of Social  Sciences Working Paper  in Demography No. 74, The Australian National  University, Canberra.

Ware,  H. 1981, A Profile of the Italian Community in Australia, Melbourne, Australian Institute  of Multicultural Affairs.

Weaver, C. (1986)  Social  security in ageing societies, in K. Davis, M. S. Bernstam  and R. Ricardo-Campbell (eds.) Below Replacement Fertility in Industrial Societies. Causes, Consequences, Policies, Population and Development Review  Supplement to Vol. 12, Cambridge, Cambridge University Press,  pp. 273–94.

Weeks, J.R. 1999, Population. An Introduction to Concepts and Issues, 7th  Edition, Wadsworth Publishing, Belmont,  CA.

Whiteford,  P. and Jackson, N.O. 1998, Demographic influences on social  security spending, paper presented to the 10th  Biennial Conference of the Australian Population Association, University of Queensland, Wadsworth Publishing, Brisbane.

Wilson,  C. (ed.) 1985, The Dictionary of Demography, Basil  Blackwell Ltd, Oxford.

Wilson,  C. (2001)  Implications of global demographic convergence for fertility  theory,  paper  presented to the International Union for the Scientific  Study  of Population Working Group on Low Fertility, International Perspectives on Low Fertility:  Trends,  Theories  and Policies, 21–23 March.

Young,  C. 1989 ‘Australia’s  population. A long-term view’,  Current Affairs Bulletin, 65(12),  pp. 4–11.

Young,  C. 1990, ‘The Impact of Demographic Change on AustraliasLabour Force with Reference to the Special Role of Women, Working Papers  in Demography, No.19, The Australian National  University, Canberra.

[ Return to Top   Return to Section ]

Useful web sites
Australian Bureau of Statistics’—contains downloadable  demographic and socioeconomic data,  including information on concepts, projections and so on.
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.
Australian Department of Health  and Aged Care—with a link to the National  Strategy  for an ageing
Australian Institute  of Family  Studies—with links  to databases and publications.
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.
Australian Department of Immigration and Multicultural Affairs.
Australian Population Association—includes a Population Facts booklet  and downloadable, related information.
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.
United  States.  Bureau of the Census—contains demographic information and data  on population ageing in the United  States.
United  States.  Social  Security  System—contains reports  for 1999 of special interest  in relation  to population ageing.
Demographic research on population ageing by the United  Nations—includes links  to many  other age-related web  sites.
United  Nations Economic  Commission for Europe—contains details  of work  by the United  Nations on population ageing.
United  Nations—contains a number of other United  Nations’ resources.
United  Nations—includes references on fertility  data  for a large  number of countries and regions.
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.
Social  Science—information gateway to a huge  range  of sites,  including for the 1970 British cohort study.
Economic  consequences of population ageing.
Global  trends  in population, health, economic factors etc.
Easy-to-use, two-step database enabling comparison of statistical information for United  Nations’countries.
Set of links  to selected, evaluated and annotated Internet  resources relevant to demography.
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.

[ Return to Top   Return to Section ]

Last updated: