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Abstract and Figures

The COVID-19 pandemic has caused extensive disruption to economies and societies across the world. In terms of demographic processes, mortality has risen in many countries, international migration and mobility has been widely curtailed, and rising unemployment and job insecurity is expected to lower fertility rates in the near future. This paper attempts to examine the possible effects of COVID-19 on Australia's demography over the next two decades, focusing in particular on population ageing. Several population projections were prepared for the period 2019-41. We formulated three scenarios in which the pandemic has either a light and short-lived impact, a moderate impact lasting 3-4 years, or a severe impact lasting up to a decade. We also created two hypothetical scenarios, one of which illustrates Australia's demographic future in the absence of a pandemic for comparative purposes, and another which demonstrates the demographic consequences if Australia had experienced excess mortality equivalent to that recorded in the first half of 2020 in England & Wales. Our projections show that the pandemic will probably have little impact on numerical population ageing but a modest effect on structural ageing. Had Australia experienced the high mortality observed in England & Wales there would have been 19,400 excess deaths. We caution that considerable uncertainty surrounds the future trajectory of COVID-19 and therefore the demographic responses to it. The pandemic will need to be monitored closely and projection scenarios updated accordingly.
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Will the COVID-19 pandemic affect population ageing in Australia?
Tom Wilson*
The University of Melbourne
* Corresponding author. Email:
Jeromey Temple
The University of Melbourne
Elin Charles-Edwards
The University of Queensland
The COVID-19 pandemic has caused extensive disruption to economies and societies across
the world. In terms of demographic processes, mortality has risen in many countries,
international migration and mobility has been widely curtailed, and rising unemployment and
job insecurity is expected to lower fertility rates in the near future. This paper attempts to
examine the possible effects of COVID-19 on Australia’s demography over the next two
decades, focusing in particular on population ageing. Several population projections were
prepared for the period 2019-41. We formulated three scenarios in which the pandemic has
either a light and short-lived impact, a moderate impact lasting 3-4 years, or a severe impact
lasting up to a decade. We also created two hypothetical scenarios, one of which illustrates
Australia’s demographic future in the absence of a pandemic for comparative purposes, and
another which demonstrates the demographic consequences if Australia had experienced
excess mortality equivalent to that recorded in the first half of 2020 in England & Wales. Our
projections show that the pandemic will probably have little impact on numerical population
ageing but a modest effect on structural ageing. Had Australia experienced the high mortality
observed in England & Wales there would have been 19,400 excess deaths. We caution that
considerable uncertainty surrounds the future trajectory of COVID-19 and therefore the
demographic responses to it. The pandemic will need to be monitored closely and projection
scenarios updated accordingly.
Key words
COVID-19; population projections; scenarios; Australia; population ageing
1. Introduction
The COVID-19 pandemic has caused severe economic and social disruption across the world.
To try to limit the spread of the disease, many governments have implemented temporary
restrictions on personal mobility, the crossing of state and international borders, social
gatherings, workplace attendance, and which retail establishments are allowed to remain
open. As a consequence, economic activity has declined and unemployment risen, resulting in
severe financial stress for many households. Many social and recreational activities, including
visiting family and friends, attending sports and entertainment venues, and going on holiday,
have been restricted. The majority of the world’s population has been impacted to some
degree (IOM 2020).
The demographic effects of the pandemic are being felt across all three of the key
demographic processes of fertility, mortality and migration. In some countries the pandemic is
exacting a terrible mortality toll, while in others the impact appears to be very minor (Johns
Hopkins University 2020). International travel, and therefore international migration, has
been severely curtailed (Connor 2020) and rates of internal migration are also likely to be
impacted, although the scale is unclear. Internal migration strongly procyclical, suggesting
that the impending recession is likely to dampen internal flows in advanced economies (Van
der Gaag and Van Wissen 2008). The effect on fertility is yet to become apparent, but the
literature suggests a likely lowering of fertility for at least a short period (Matysiak et al.
This paper considers the possible effects of COVID-19 on Australia’s demographic trends in
coming years, focusing particularly on population ageing. Population forecasting is
particularly hazardous at this time and there is considerable uncertainty about even the short-
run future of the pandemic and its demographic consequences. It is possible there will be an
additional wave of COVID-19, like the Spanish flu a century ago (Martini et al. 2019).
Government decisions about the re-opening of borders will greatly affect international
migration trends, but these are very hard to forecast at present. And there will undoubtedly be
consequences which last well beyond the end of the pandemic which are extremely difficult to
predict. We therefore present several scenarios which consist of possible trajectories for
Australia’s demography over the next two decades. Our aim is to describe a plausible range of
demographic impacts, while acknowledging that our data and knowledge is limited in the
present circumstances.
Population ageing is measured in terms of both numerical ageing (an increase in the absolute
size of the older population) and structural ageing (an increase in the proportion of the
population in older age groups). We define the older population in two ways: first, in the
traditional manner where it consists of those aged 65 years and above, and second, an
alternative in which it comprises the population with 15 years or less of remaining life
expectancy (Sanderson and Scherbov 2019; Wilson and Temple 2020). Following this
introduction, we briefly describe the data, projection model, and scenario assumptions. The
next section presents projection results for Australia’s older population according to the
various scenarios, before we finish with a final section of discussion and concluding remarks.
2. Data, methods, and scenarios
Population projections for Australia were prepared using a cohort-component model
incorporating directional immigration and emigration flows based on a movement population
accounts framework (Rees 1984). There is insufficient space here to describe details of the
model, but they are available elsewhere (see Charles-Edwards et al. 2020). All input data were
sourced from the Australian Bureau of Statistics (ABS), including census data obtained from
the ABS TableBuilder Pro system (ABS 2020a). The projections begin with a 30 June 2019
‘jump-off’ point and we present results out to 2041.
Projection scenarios were formulated in terms of the Total Fertility Rate (TFR), annual Net
Overseas Migration (NOM), and life expectancy at birth (e0). We devised three plausible
COVID-19 scenarios, one ‘what if?’ scenario describing what would have happened if the
pandemic had caused a mortality increase similar to that recorded recently in the UK, and one
‘No pandemic’ scenario for comparison. The five scenarios are thus:
(1) a Light impact scenario where economic and demographic trends bounce back strongly
over 1-2 years,
(2) a Moderate scenario where the effects are felt for 3-4 years,
(3) a Severe impact scenario with an extended economic depression of 5-10 years,
(4) a No Pandemic scenario to illustrate Australia’s likely demographic direction in the
absence of the pandemic, and
(5) a ‘what-if?’ High Mortality scenario to illustrate what would have happened if Australia
had experienced excess mortality similar to that of the UK.
In the Light, Moderate and Severe scenarios we assumed no changes to the long-run
trajectory of mortality given that the pandemic’s impact on the number of deaths in Australia
appears limited (ABS 2020b). For the first four scenarios life expectancy for females was
assumed to increase to 88.8 years by 2040-41, while for males we assumed an increase to
86.3 years. The TFR and NOM assumptions for the scenarios are illustrated in Figure 1.
[Figure 1 about here]
In the Light scenario the impact of COVID-19 is short-lived and both the economy and
demography recover rapidly. The TFR drops a little to 1.65, but then bounces back with
recuperation of births postponed during the pandemic. Long-run fertility is assumed to consist
of a TFR of 1.70 given the gradual fall in Australian fertility in recent years. In this scenario
NOM falls to 150,000 in the 2020-21 financial year but bounces back quickly and strongly
due to government actions to attract international students and workers back to grow the
In the Moderate scenario the economic and demographic impacts of COVID-19 last a few
years. The TFR drops to 1.55 before recovering over the next few years and then experiencing
some recuperation. NOM falls to 75,000 in 2020-21 and then recovers over the next few
years, stabilising at 210,000 per annum. International students return gradually and the
Australian border is opened progressively to more countries, and more migrants, over time.
The Severe scenario describes a more challenging set of circumstances. Overseas and
Australian economies remain in deep recession for many years. COVID-19 proves hard to
control. Unemployment remains high, international student numbers take a long time to
recover, and the moribund state of the economy means demand for overseas workers is
limited. The TFR plunges to 1.45 and takes many years to recover, eventually stabilising at
1.70. NOM drops to zero in 2020-21 and also takes a long time to recover.
In the what-if? High Mortality scenario, we assumed that age- and sex-specific death rates
had increased by the same proportional amount as those in England & Wales between 2019
and 2020. We obtained counts of deaths in England & Wales by sex and broad age group in
2019 and in 2020 up to 12th June from the Office for National Statistics website (ONS 2020).
We made the optimistic assumption of no further excess mortality in England & Wales after
12 June and ‘nowcast’ the remaining deaths for 2020 by borrowing deaths for the same period
in 2019. We then estimated the ratios of deaths in 2020 to 2019 by sex and broad age group.
With only small differences in population numbers between 2019 and 2020 these ratios
provide approximations of the ratios in age-sex-specific death rates. These age-sex-specific
ratios were multiplied by 2018-19 financial year death rates for Australia to estimate the
2019-20 set of pandemic death rates. The impact is assumed to be short-lived and after this
one year of high mortality, death rates return to the mortality rates used in all other scenarios.
The fertility and overseas migration assumptions were set to the values chosen for the
Moderate scenario.
The first three COVID-19 scenarios were informed by three broad sets of information
sources. First, we examined the economic outlook of the Reserve Bank of Australia (RBA
2020) in its Statement on Monetary Policy. This includes three economic scenarios for
Australia over the next few years: baseline gradual recovery, slower recovery, and faster
recovery. These scenarios are described by the RBA in terms of macroeconomic indicators,
and do not include demographic details, but they provided a helpful starting point for our
scenario thinking. Second, we examined the academic literature on the effects of recessions,
large disease outbreaks, and major disasters on fertility and migration. Broadly, this suggests
that in western countries both fertility and internal migration tend to be pro-cyclical, i.e.
fertility reduces with economic recession (e.g. Matysiak et al. 2020; Sobotka et al. 2011) as
does internal migration (e.g. van der Gaag and van Wissen 2008; Alvarez-Contras et al.
forthcoming). Recent evidence from the 2008 global financial crisis shows worldwide
international migration was impacted, with strongest reductions in labour migration, and a
lesser impact on humanitarian, family and other movements (Betts and Willekens 2009).
However, the impacts on NOM attributable to COVID-19 are likely to be much stronger and
affect numerous visa classes due to the closure of national borders, and the timing of
reopening of different sections of the economy (e.g. international students returning to tertiary
education institutions). Third, we consulted with a select number of Australian fertility and
migration experts, receiving advice about possible trends in the TFR and NOM over the next
few years, and feedback on our draft scenario assumptions.
3. Results
The total projected population of Australia under the five scenarios for selected years in the
projection horizon is presented in Table 1. From an estimated population of 25.4 million in
2019, Australia’s population is projected to grow to between 28.7 to 29.9 million by 2031
according to our scenarios, and then to between 32.1 and 33.6 million by 2041. The difference
with the No Pandemic scenario by 2031, when all scenarios have converged in their
assumptions, is 147,000 in the Light scenario, 487,000 in the Moderate scenario, 1.20 million
in the Severe scenario, and 494,000 in the High Mortality scenario. By 2041 the differences
are, respectively, 181,000, 592,000, 1.41 million, and 595,000.
[Table 1 about here]
Projected population ageing is illustrated in Figure 2. The upper graph shows that the
population aged 65+ hardly varies between the four scenarios. From 4.0 million in 2019, it is
expected to grow to 6.7 million by 2041 according to all scenarios, with only tiny differences
which are imperceptible on the graph. The population with 15 or fewer years of remaining life
expectancy grows from 2.1 million in 2019 to 3.4 million by 2041 according to all scenarios,
with the exception of the what-if High Mortality scenario where the 2019-20 fall in life
expectancy temporarily increases this population by 213,000. This is because RLE < 15 years
is, by definition, based on mortality conditions.
However, projected structural population ageing, depicted in the lower graph of Figure 2,
shows a modest degree of variation between scenarios. The share of the total population aged
65+ of 15.9% in 2019 increases to 20.0% by 2041 in the No Pandemic scenario. In the other
scenarios it increases to between 20.2% (Light) to 20.9% (Severe) by 2041. The proportion
aged 65+ in the High Mortality scenario is almost exactly the same as in the Moderate
scenario. The share of the population with 15 or fewer years of remaining life expectancy
(RLE < 15) similarly varies a little between scenarios. It increases from 8.3% in 2019 to
10.2% by 2041 in the No Pandemic scenario and, in the other scenarios, between 10.3%
(Light) and 10.7% (Severe). In the High Mortality the value temporarily increases to 9.3%
compared to 8.4% in the other scenarios.
[Figure 2 about here]
The immediate cause of the variation in structural ageing, but not numerical ageing, is shown
in Figure 3, which presents the projected age structure of Australia’s population in 2031. As
the graph shows, differences between scenarios are minimal above age 65, but quite large in
the childhood ages up to about age 12 and in the younger adult ages where overseas migration
is highest. Thus, the older population (measured either as those aged 65+ or RLE<15 years)
remains much the same while the total population varies between scenarios, and therefore the
percentages in the older ages vary. Population age structure differences are created by
variations in NOM assumptions for the younger adult ages and variations in both TFR and
NOM assumptions for the childhood ages (with NOM affecting the size of the childbearing-
age population).
[Figure 3 about here]
The effect of the different scenarios on projected births and deaths is illustrated in Figure 4.
The lack of variation in the projected number of deaths between the first four scenarios is
unsurprising because the alternative NOM assumptions make very little difference to the size
of the population at older ages (where most deaths occur) and the various TFR pathways
make no difference over the projection horizon. In contrast, both the TFR and NOM
variations across scenarios generate quite different outcomes for the projected number of
births. Under the Severe scenario, for example, the annual number of births falls to a low of
17% below that in the No pandemic scenario during the years 2022-25.
The impact of the High Mortality scenario on the number of deaths which would have
occurred in 2019-20 under UK-like pandemic conditions is substantial. While 163,100 deaths
are modelled for the first four scenarios, the High Mortality scenario results in 182,500 deaths
in 2019-20, which equates to 19,400 excess deaths (12% higher than ‘expected’). In
subsequent years the numbers of deaths projected in the High Mortality scenario are
marginally lower than in the other scenarios.
[Figure 4 about here]
4. Discussion and concluding remarks
One of the key demographic challenges facing Australian policy makers is ensuring the
successful adaptation of the economy, institutions and broader society to continued population
ageing. Until now, there has been limited evidence on the demographic consequences
following the COVID-19 pandemic, including the effects on structural and numerical ageing.
At the onset of the outbreak in early 2020, Piggott (2020) queried: “How will Australia’s age
structure change in the future? Will COVID-19 make any difference to it?” (Piggott 2020).
The pathway through which population ageing (both structural and numerical) may be
impacted by COVID-19 is through changes to the key demographic processes of fertility,
mortality and migration.
In this paper we have sought to outline the impact of possible changes to underlying
demographic parameters attributable to COVID-19 on the number and relative size of the
older Australian population. Overall, we find by 2041, COVID-19 would reduce the total
population by between 181,000 (Light scenario) to 1.41 million people (Severe scenario).
However, in terms of numerical ageing, the total number of older Australians (aged 65 and
over) barely shifts under any of these scenarios. This finding is not unexpected, given the
young age structure of migrants to Australia, and thus limited change in the older population
through this pathway. Relative to other countries, impacts on mortality at older ages have also
been small. By reducing the rejuvenation impact of immigration on age structure, and the
possible fall in fertility arising due to COVID-19, we do, however, observe a modest increase
in structural ageing. By 2041, we projected about 20.0% of the population to be aged over 65
in a no pandemic scenario, relative to between 20.2% (Light) to 20.9% (Severe) in pandemic
conditions. It is important to recall that under all scenarios, levels of NOM return to levels in
excess of 200,000 per annum. Levels of structural ageing would be considerably higher
should NOM rebound to a lower level. Much of this will depend not only on cyclical
responses due to international and domestic macroeconomic conditions, but also due to
decisions made by Australian policy makers regarding long-term levels of NOM. Prior to
COVID-19, both major political parties were strongly supportive of high levels of
immigration (Betts and Birrell 2019).
Our modelling also provides evidence of the efficacy of the Australian response in reducing
excess mortality due to COVID-19. In a senate enquiry into the Australian Government’s
response to COVID-19 during May, the Australian Chief Medical Officer suggested
approximately 14,000 deaths had been avoided due to border closures, social distancing and
the broader public health response (McCauley 2020). Our modelling indicates, that over the
full 2019-20 financial year, if Australia had followed the example of England & Wales, an
excess of 19,400 deaths may have occurred in 2019-20. This is a very significant figure and
12% higher than the No Pandemic scenario. To place this figure in context, in 2018
Australia’s leading cause of death was Ischaemic heart disease, accounting for 17,533 deaths
(ABS 2019). The influenza outbreak (the ‘Spanish flu’) in Australia one century ago,
accounted for between 15,000 and 16,000 deaths during 1919 (Curson and McCracken 2006),
making the annual death count of that year about 30% higher than it would otherwise have
By the end of June 2020, 104 deaths attributable to COVID-19 had been reported
(Department of Health 2020). However, the full impact of the pandemic on deaths in
Australia is yet to be revealed with some preliminary evidence suggesting the possibility of
unaccounted COVID-19 deaths due to attribution to other causes (Bennett 2020). Moreover,
the shutdown and broader public health response may have generated negative externalities
impacting morbidity and mortality. As raised by Bennett (2020) “Has the deferral of elective
surgeries affected the death rate? Has there been a death toll associated with people being
discouraged from visiting clinics or hospitals for other illnesses? Have the stresses of
lockdown and financial uncertainty led to a rise in domestic violence or suicide?
Our analysis has considered the impact of COVID-19 on population ageing, with the impact
being minimal on numerical ageing and modest on structural ageing. However, at the
individual level, older Australians have been at an elevated risk of COVID-19 infection and
mortality. As of June 2020, approximately 29% of all COVID-19 cases have been attributed
to people aged 60 and over, with this same group accounting for 97% of COVID-19 deaths
(authors calculations from Department of Health 2020). The pandemic is likely to have had a
considerable impact on individual ageing experience of older Australians. For example,
concerns have arisen about delayed presentation and treatment of non-COVID-19 conditions,
postponement of elective surgeries and increased risk of symptoms of depression and anxiety
(Holt et al. 2020). The United Nations has highlighted that with lockdowns and reduced care,
elder abuse may be increasing (United Nations 2020).
The broader macroeconomic impacts of COVID-19 on the Australian labour market have had
a deleterious impact on mature age workers (Temple and McDonald 2020). As the economic
ramifications of the pandemic play out of the next few years, the superannuation balances of
retirees may also be impacted. The ongoing consequences of COVID-19 for older Australians
requires detailed analyses to support their wellbeing. Indeed, the current Royal Commission
into Aged Care Quality and Safety has issued a call for submissions on the impact of COVID-
19 “to understand the impact of the pandemic upon older Australians, their families and their
carers, in aged care facilities and receiving home care (Royal Commission into Aged Care
Quality and Safety 2020). Ensuring that older Australians are supported through this critical
COVID-19 recovery period will support the Governments policy aim of ensuring successful
adaptation to population ageing.
Of course, our study contains a number of limitations. The most obvious one is the huge
amount of uncertainty surrounding the future of the COVID-19 pandemic and its impacts on
Australia’s economy and demography. Much depends on global efforts to reduce infections,
the timing and effectiveness of a vaccine, the length and depth of recession, and political
responses to the situation. Our scenarios represent merely possible futures and certainly not
forecasts with any degree of certainty. To enable comparisons of alternative effects of the
pandemic between scenarios, we selected long-run assumptions which are identical. Many
other scenario assumptions could have been chosen. It is entirely plausible, of course, that
NOM does not return to the 200,000+ annual gains of recent years. At the same time, we
cannot entirely dismiss higher levels of NOM either. A long-lasting recession may push
Australia’s fertility rate to record low levels, similar to those observed recently in many
European countries. And the modest fertility recuperation we have assumed in some scenarios
may fail to materialise. We also assumed no change in Australia’s long-run mortality
trajectory for most scenarios, though it is possible that some mortality effects of COVID-19
may only become apparent in the future. In addition, our selected age profiles of fertility,
mortality, immigration and emigration, based on recent years of data, may also experience
alterations due to the pandemic. In the context of all this uncertainty, the situation will need to
be monitored closely and population projections updated regularly. Finally, we only examined
the possible demographic impacts COVID-19 at the national level, but its effects will
certainly be felt unevenly across the country. Further work will be required to examine the
geographical variations in the demographic consequences of the pandemic.
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Ethics approval
This project has been granted ethics approval by the University of Queensland
Availability of data
Population projections data is available on request from the authors.
Funding source
Wilson and Temple’s contributions to this paper were supported by the Australian Research
Council’s (ARC) Centre of Excellence in Population Ageing Research (project number
We gratefully acknowledge the helpful advice and feedback on our projection scenario
assumptions from several experts, particularly Edith Gray and Peter McDonald who provided
detailed advice on fertility and overseas migration. However, the final scenario assumptions
and all flaws in them remain solely the responsibility of the authors.
Competing interests
None declared.
Table 1: The projected total population of Australia under the five scenarios, 2019-2041
No pandemic
High Mortality
Source: authors’ projections
Figure 1: TFR and annual NOM assumptions of each scenario
Source: ABS; authors’ projections
Notes: For the TFR, the Light, High Mortality and No pandemic scenarios are the same.
Figure 2: Projected numerical and structural ageing of Australia’s population under the five
scenarios, 2019-41
Source: authors’ projections
Note: The % aged 65+ in the High Mortality is almost exactly the same as in the Moderate scenario.
Figure 3: The projected age structure of Australia’s population under the five scenarios in
Source: authors’ projections
Figure 4: Projected births and deaths under the five scenarios
Source: authors’ projections
Note: The number of births in the High Mortality is the same as in the Moderate scenario.
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This study investigates how the changes in labour market conditions and economic growth were associated with fertility before and during the Great Recession in Europe in 2002–2014. In contrast to previous studies, which largely concentrated at the country level, we use data for 251 European regions in 28 European Union (EU) member states prior to the withdrawal of the United Kingdom in January 2020. We apply three-level growth-curve model which allows for a great deal of flexibility in modelling temporal change while controlling for variation in economic conditions across regions and countries. Our findings show that fertility decline was strongly related to unemployment increase; this relationship was significant at different reproductive ages. Deteriorating economic conditions were associated with a stronger decline in fertility during the economic recession as compared with the pre-recession period. This evidence suggests the salience of factors such as broader perception of uncertainty that we could not capture in our models and which rose to prominence during the Great Recession. Furthermore, strongest fertility declines were observed in Southern Europe, Ireland and parts of Central and Eastern Europe, i.e. countries and regions where labour market conditions deteriorated most during the recession period. In Western Europe, and especially in the Nordic countries, fertility rates were not closely associated with the recession indicators.
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In Europe in 1918, influenza spread through Spain, France, Great Britain and Italy, causing havoc with military operations during the First World War. The influenza pandemic of 1918 killed more than 50 million people worldwide. In addition, its socioeconomic consequences were huge. “Spanish flu”, as the infection was dubbed, hit different age-groups, displaying a so-called “W-trend”, typically with two spikes in children and the elderly. However, healthy young adults were also affected. In order to avoid alarming the public, several local health authorities refused to reveal the numbers of people affected and deaths. Consequently, it was very difficult to assess the impact of the disease at the time. Although official communications issued by health authorities worldwide expressed certainty about the etiology of the infection, in laboratories it was not always possible to isolate the famous Pfeiffer’s bacillus, which was, at that time, deemed to be the cause of influenza. The first official preventive actions were implemented in August 1918; these included the obligatory notification of suspected cases and the surveillance of communities such as day-schools, boarding schools and barracks. Identifying suspected cases through surveillance, and voluntary and/or mandatory quarantine or isolation, enabled the spread of Spanish flu to be curbed. At that time, these public health measures were the only effective weapons against the disease, as no vaccines or antivirals were available. Virological and bacteriological analysis of preserved samples from infected soldiers and other young people who died during the pandemic period is a major step toward a better understanding of this pandemic and of how to prepare for future pandemics.
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This article investigates the impact of the global economic crisis on international migration. Empirical evidence is scarce and mainly captures short-term consequences. The study covers (1) international migration theory, (2) the impact of past financial crises on international migration, and (3) published expert opinions, studies and discussions. The impact varies by reason for migration and by migrants’ employment status. Labour migration is affected most, in particular migration of low-skilled persons. Political and environmental refugees, marriage migration and family reunion will not be much affected. Remittances are affected less than predicted. The management of migration during periods of economic downturns should be guided by short- and long-term perspectives on the role of migration in development.
Although the decline in the level of internal migration has been the focus of growing scholarly attention, little attention has been paid to countries with increasing or stable intensities. As a result, it is not clear why internal migration is declining in some countries but not in others. This paper seeks to address this gap by establishing variations in internal migration trends in 18 OECD countries and determining the economic, demographic and social factors underpinning them. We assemble a time series of annual interregional migration intensities from 1996 to 2018. We find that all non‐European countries report a downward trend, whereas in Europe, eight countries exhibit an upward trend, four do not display a significant trend in either direction, and Poland shows a modest decline. We use a country fixed‐effect dynamic panel data model to distinguish long‐ and short‐term associations. The results show that a reduction in the proportion of young adults and regional inequalities decreases interregional migration whereas a rise in information technologies and net international migration increases interregional migration in the long term. Only regional income inequalities affect the intensity of migration in both the short and the long term. Although the different paces at which these factors evolve explain some of the heterogeneity in internal migration trends, they do not fully account for the downward trend in non‐European countries. Panel time series are valuable for understanding internal migration trends and effort should be geared towards the inclusion of more countries, extended periods and additional explanatory variables.
This article reviews research on the effects of economic recessions on fertility in the developed world. We study how economic downturns, as measured by various indicators, especially by declining GDP levels, falling consumer confidence, and rising unemployment, were found to affect fertility. We also discuss particular mechanisms through which the recession may have influenced fertility behavior, including the effects of economic uncertainty, falling income, changes in the housing market, and rising enrollment in higher education, and also factors that influence fertility indirectly such as declining marriage rates. Most studies find that fertility tends to be pro-cyclical and often rises and declines with the ups and downs of the business cycle. Usually, these aggregate effects are relatively small (typically, a few percentage points) and of short durations; in addition they often influence especially the timing of childbearing and in most cases do not leave an imprint on cohort fertility levels. Therefore, major long-term fertility shifts often continue seemingly uninterrupted during the recession—including the fertility declines before and during the Great Depression of the 1930s and before and during the oil shock crises of the 1970s. Changes in the opportunity costs of childbearing and fertility behavior during economic downturn vary by sex, age, social status, and number of children; childless young adults are usually most affected. Furthermore, various policies and institutions may modify or even reverse the relationship between recessions and fertility. The first evidence pertaining to the recent recession falls in line with these findings. In most countries, the recession has brought a decline in the number of births and fertility rates, often marking a sharp halt to the previous decade of rising fertility rates.
The 1918-1919 influenza pandemic stands as one of the greatest natural disasters of all time. In a little over a year the disease affected hundreds of millions of people and killed between 50 and 100 million. When the disease finally reached Australia in 1919 it caused more than 12,000 deaths. While the death rate was lower than in many other countries, the pandemic was a major demographic and social tragedy, affecting the lives of millions of Australians. This paper briefly assesses the impact of the pandemic on Australia and NSW with particular reference to the demographic and social impact and the measures advanced to contain it.