Productive engagement across the life course: paid work and beyond.
Loh, Vanessa ; Kendig, Hal
Introduction
The Commonwealth Government's Intergenerational Reports (IGRs)
highlight concerns that population ageing is increasing costs to the
state and reducing workforce supply (Treasury 2010). Related research
studies by the Australian Institute of Health and Welfare (AIHW; Jenkins
et al. 2003) and the National Centre for Social and Economic Modelling
(NATSEM; Nepal et al. 2011a) present the projected care needs of an
ageing population and projected shortfalls of informal caregivers.
Demographic ageing, increasing longevity and continuing economic
uncertainty have also generated intense concern for the sustainability
of retirement income systems and care systems in developed and
developing countries around the world (OECD 2006, 2011).
The Government's third and most recent IGR (Treasury 2010)
concluded that maintaining workforce participation and increasing
productivity are priorities for Australia in responding to the future
challenges of economic uncertainty and population ageing. Positive views
on the role of older people have been presented by National Seniors
Australia (NSA 2009) and the Treasury's Advisory Panel on the
Economic Potential of Senior Australians (EPSA; Treasury 2011). The
Government Response to the final EPSA report (Treasury 2012) accepted
recommendations for action in key areas including active ageing,
volunteering and age discrimination, outlining a number of important
initiatives now underway that have prospects for constructively enabling
more contributions by older people. According to the Council on the
Ageing, the most important is the 10 year Plan on Positive Ageing, which
'provides an opportunity to develop a comprehensive approach across
all aspects of economic and social life to ensure older people are able
to participate fully as citizens and members of society' (COTA
2012: 2). A common theme in these reports is the importance of
recognising, enabling and assisting Australians to continue productive
contributions as they age.
However, there has been limited research outside these commissioned
reports containing population trends and projections specifically for
older age groups, and past research typically has not presented
corresponding data on younger age groups. Providing comparative data for
those in early, middle and later life is important for research on
productive engagement over the life course. Therefore, the main
objective of the present study is to present an independent descriptive
analysis of recent data on the productive contributions of older
Australians alongside comparative data for younger age groups.
This study aims to contribute to the literature by 1) providing a
review of recent research and 2) presenting comprehensive data on
engagement in both paid and unpaid productive activities by younger,
middle and older age groups, as an indication of change over the life
course, which may act as a useful reference for future research on
productive engagement in Australia. The results are disaggregated by key
demographic and human capital factors that have previously been
associated with productive engagement, including age, gender, health,
education, income, occupation and marital status. The paper concludes
with a summary of research directions and policy actions that could
enable productive engagement in mid and later life.
Theoretical framework
Human capital
Opportunities and necessities to make economic and social
contributions can depend on an individual's physical and mental
health capacities, their motivations and skills, their family and
community context, competing pressures on time use and the structural
incentives and constraints imposed by employment markets and public
policies (WHO 2010; NSA 2012). The idea that personal and social
factors, such as health and education, are resources that can either
enable or constrain an individual's ability to make productive
contributions, and that these resources are modifiable, is consistent
with the concept of human capital (Mirowsky & Ross 1998). A recent
working paper by the Productivity Commission (Shomos 2010: 1) linking
literacy and numeracy skills to labour market outcomes defined human
capital as 'the set of attributes that individuals possess,
including knowledge, skills, work experience, health and intangible
characteristics such as motivation'. According to human capital
theory, such resources may be acquired or enhanced through education,
training or other informal means including 'the experiences of
undertaking daily activities at home or at the workplace' (Shomos
2010: 1). The social policy implications of this are that an investment
in human capital can yield positive returns by increasing future
productivity. Supporting this view and the Government's current
interest in improving human capital, recent research in Australia has
found that improvements in education, training and health are related to
increased wages and labour market productivity (McDonald 2010; Shomos
2010; Nepal et al. 2011b).
Given that the return on investments in human capital is likely to
appear in the medium to longer term, a life course approach is useful
for examining productive activities in middle to later life. A key idea
consistent with contemporary developmental life course perspectives (for
example, Elder 1995) is that a person's life course or
developmental trajectory is influenced by earlier life experiences and
exposures, which have in part been shaped and reinforced by age-,
gender- and other socially-graded norms, expectations and institutions
within the particular cultural context (Moen 1996; Moen & Spencer
2006; Hershey et al. 2010). In addition, because education, income,
health and productive engagement can vary throughout the life course,
examining productive engagement not only in middle to later life, but
also amongst earlier age groups will add to our understanding of how
productive engagement varies across the life course by gender, age and
human capital resources.
Marital status and occupation are not typically included in the
human capital framework, apart from use as demographic control
variables, but they may also be useful indicators of life-long personal
resources and available kinship networks (Kendig et al. 2007; Kjellsson
2013). Marital status may be positively associated with productive
engagement when one's personal resources are extended to include
the resources of their partner (Glass et al. 1995; Hesketh et al. 2011).
Being partnered can widen one's kinship network and increase the
probability of providing care for children, grandchildren and adult
relatives; it has also been associated with other benefits such as good
health (Kendig et al. 2007), particularly for men, that may directly
affect productive engagement. In addition to its association with
education and income, occupation can provide further human capital
relevant to engagement in productive activities. For example,
individuals in managerial and professional jobs may have more
opportunity to develop further work-related skills and knowledge, while
those in more physical or manual jobs may experience limitations
including poorer health and wage outcomes (Kjellsson 2013).
Productive ageing
The global significance of ageing well and ageing productively has
been brought to attention by three major worldwide trends. First, the
demographic trend of people living longer increases the potential for
continued productive engagement, particularly if the prevalence and/or
negative impacts of poor health and chronic disease are reduced (Mor
2005; Mathers 2007). Second, there has been a corresponding
sociocultural trend in which people have been showing less inclination
to retire from the paid workforce in the more traditional sense, with
many choosing (if possible) to remain active and continue productive
activities (Morrow-Howell et al. 2001a). Third, the global trend of
continuing economic uncertainty highlights the increasing need for
productive engagement across all age groups (Chomik & Piggott 2012).
While the United States has experienced recent rises of unemployment
among older people, the main effect of the recent global financial
crisis (GFC) in Australia appears to have been to encourage some delays
in planned retirement and adjustments to lower expenditure for those
reliant on investment returns for retirement income (O'Loughlin et
al. 2010; Kendig et al. in press).
The concept of productive ageing emerged in the United States in
the 1980s, largely as a positive reaction against the dominant, ageist
view of ageing as a process of declining physical and cognitive health
and functioning, resulting in increased dependency and dwindling
contributions (O'Reilly & Caro 1994). The problem with this
dominant view is that it devalued older people, failing to acknowledge
their ongoing contributions and potential for productive engagement in a
range of activities, not only paid work. In contrast, the productive
ageing perspective recognises that individuals can and often do continue
their engagement in activities that have socioeconomic value within
their social context as they age.
Although the meaning of productive ageing can vary greatly across
different cultures, societies and individuals (Carr et al. 2013),
productive ageing may be defined as the engagement of older people in
activities that produce, or develop the capacity to produce, goods or
services that are either paid for or unpaid, but otherwise would have to
be paid for (O'Reilly & Caro 1994; Bass & Caro 2001).
Productive ageing can be defined at the societal level or at the
individual level. At the societal level, productive ageing refers to
older people's engagement in activities that can be assigned some
social or economic value such as contributions of money, time, effort
and information. At the individual level, productive ageing includes
activities that improve an individual's physical or mental health,
which have potential positive carry-on effects of decreasing dependency
and increasing capacity to contribute at the broader social or economic
level (Birren 2001). Defined broadly, productive activities can include
paid work, volunteering, caregiving, and other informal help to family
and friends, as well as activities that have indirect value to the
broader community and directly benefit the individual, such as self-care
or self-maintenance (that is, looking after one's self), and
further education and training (Morrow-Howell et al. 2001a; Kendig &
Browning 2010).
A recent Australian review has warned that the concept of
productive ageing can have pejorative, normative connotations,
particularly when less privileged workers are expected to continue
working at the expense of a period of personally-rewarding, meaningful
activity including leisure after retirement (Carr et al. 2013). Thus, it
is important to note that the alternative to productive ageing or
activities is not necessarily unproductive ageing or activities
(Morrow-Howell et al. 2001b; Walker 2008). Rather, Morrow-Howell and
colleagues (2001b) suggest that productive ageing should be viewed as
one of several goals that one may wish to pursue in later life, and
different individuals may have different goals for later life, all of
which have value. Thus, although productive ageing can have real and
immediate economic value to both the individual and the broader
community, it is not an ultimate goal that should be imposed on all
people. In our view, public policy should aim to promote and support or
enable productive ageing as an option for those who wish to continue
productive activities in later life.
Prior research
With the exception of a few published studies (such as Dosman et
al. 2006; Fernfindez-Ballesteros et al. 2011), the majority of prior
research on productive ageing has been conducted within the United
States. As Hinterlong (2008) noted, research on productive ageing has
been limited in part by the lack of consensus about which activities
should count as productive. Paid work has been the most extensively
researched productive activity (for example, McDonald 2011), although
there has been increasing research on less formalised, typically unpaid
activities that are more difficult to measure, such as volunteering (for
example, Tang et al. 2010), caregiving (for example, McMunn et al.
2009), childcare and domestic work (for example, de Vaus et al. 2003).
Even less attention has been paid to individual-level productive
activities such as self-care or self-maintenance, education and training
(for example, Brandon & Temple 2006).
From his review of American research prior to 2006, Hinterlong
(2008) reported that the majority of older individuals were involved in
at least one productive activity, with a significant proportion involved
in multiple activities. Following 1,644 Americans aged 60 and over in
1986 from the Americans' Changing Lives Study (ACL) for three waves
(1986, 1989, 1994), Hinterlong (2008) also found that the majority (74
per cent) of respondents engaged in one or more productive activities at
each wave, although individuals varied significantly in the amount of
time spent on each activity and this individual variability increased
over time while mean time decreased.
Much of the research on the contributions of individuals in
Australia has typically focused on only one or two productive
activities. For example, McDonald (2011) presented useful data on paid
work participation for Australians aged 55 years and over, while Bittman
and colleagues (2007) examined how caregiving impacts the paid work
participation of Australians aged 15 to 64 years. Using data from the
Australian Bureau of Statistics (ABS), National Seniors Australia (NSA
2009) reported that 71 per cent of Australians aged 55 to 59 years of
age were engaged in paid work, which was the highest participation rate
for this group since 1978. NSA (2009) also reported that, among
Australians aged 55 and over, the percentage of those engaged in
voluntary work increased steadily between 1995 and 2006. One of the few
studies to examine multiple activities was conducted by Brandon and
Temple (2006), but did not include paid work. Their time diary data for
1,350 individuals not in the workforce and aged 55 years and over from
the 1997 Australian Time Use Survey (TUS), suggested that while all
older Australians spent time on domestic work, many (42 per cent) also
spent time in volunteering, caregiving, and childcare activities.
Gender
In an early overview of productive ageing in the United States,
O'Reilly and Caro (1994) noted significant gender differences, with
a greater proportion of women than men engaged in unpaid productive
activities such as domestic work, childrearing, caregiving and
volunteering. They also reported that more women experienced conflicts
between paid work and these unpaid activities, which could lead to a
reduction in or withdrawal from engagement in paid work earlier in life.
Consistent with a life course perspective, they noted that having an
interrupted or reduced engagement in paid work earlier in life could
also limit one's access to opportunities for paid work in later
life. Although gender was not a strong predictor of volunteering, women
tended to volunteer more than men in later life, and also tended to give
more time.
More recently, in their study of 458 healthy Spanish adults aged 55
to 75, Fernfindez-Ballesteros and colleagues (2011) reported that women
spent significantly more time performing productive activities than men,
but this was largely due to more time spent in domestic work. In
Australia, McDonald (2011) presented data from 2000 to 2010 indicating a
trend of increasing paid work engagement among people 55 years and older
in more recent years, particularly for women, although their rates of
engagement remained substantially lower than men in every year.
Interestingly, while partnered men aged 55-59 and 60-64 were more likely
than unpartnered men to be in paid work, there was little difference at
older age groups or for women. Amongst older individuals not in paid
work, Brandon and Temple (2006) also found that, controlling for age
group, marital status, education level, English as a first language,
disability status, income, vehicle ownership and geographic location,
being female increased the likelihood of engagement in volunteering and
caring activities in addition to domestic work.
Human capital and other personal resources
The literature also suggests that, health permitting, the majority
of older individuals is likely to continue engagement in a range of
productive activities as they age, although the extent of engagement can
vary greatly between individuals, particularly at older ages (Li &
Ferraro 2006; Hinterlong et al. 2007; Hinterlong 2008). Analysing data
from the 2004 Survey of Health, Ageing and Retirement in Europe (SHARE),
Erlinghagen and Hank (2006) found that being younger, having better
health and higher levels of education increased the likelihood of
engagement in volunteer work.
Similarly, Brandon and Temple (2006) found that having lower rates
of disability, being younger, widowed and more highly educated increased
the likelihood of engagement in volunteering and caring activities in
addition to domestic work amongst older Australians not in paid work.
Regarding paid work, McDonald (2011) reported higher rates of engagement
amongst older Australians with no long-term health conditions or
functional disabilities, higher education levels, greater English
proficiency and those living in a capital city. These results provide
early evidence that personal and human capital factors such as health or
disability status and educational attainment can enable or constrain
one's capacity to engage in multiple productive activities beyond
paid work.
Time as a resource and constraint
Time is another personal resource that can affect engagement in
productive activities. This is most evident in the literature providing
evidence of the potential time trade-off or competition for time between
different productive activities, usually between paid work and other
productive activities. For example, Fernfindez-Ballesteros and
colleagues (2011) found that retirees spent more time on adult
caregiving and formal volunteering than non-retirees. Similarly,
Erlinghagen and Hank (2006) found that workers were less likely than
retirees to be engaged in volunteer work. In contrast, being an informal
helper or carer increased the likelihood of engagement in volunteer
work. In a smaller American study, Tang and colleagues (2010) found that
the top two reasons for stopping volunteer work were declining health
and commitments to other productive activities including paid work,
caregiving or other volunteering programs.
Using a relatively young cross-sectional sample of 3,581 Canadians
aged 45 and older from Statistics Canada's 1998 General Social
Survey, Dosman and colleagues (2006) examined engagement in productive
activities and daily time allocation by gender and work status. They
found that compared to their still-employed counterparts, retirees spent
more time in self-care and unpaid productive work (domestic work,
caregiving and volunteering), and a small percentage (four to six per
cent) of retirees still spent on average four hours per day in paid
work. Retired men were more likely than employed men to be engaged in
volunteering and domestic work, and were also likely to spend more time
in domestic work than employed men. Surprisingly, fewer retired women
than employed women were engaged in caregiving, but of those engaged,
retired women spent more time caregiving than employed women. This mixed
finding highlights the value of including time spent as a measure of
productive engagement.
In their Australian study, Bittman and colleagues (2007) found
that, among those initially in full-time work, the likelihood of moving
to either part-time or no work was greater among carets than non-carets.
In addition, among those initially not in the labour force, carers were
much less likely than non-carers to begin either full-time or part-time
work, suggesting that caring had both immediate and longer-term effects
on engagement in paid work. These findings underscore the value of a
life course approach that considers engagement not only in middle or
later life, but also at earlier ages. Baxter and colleagues (2007)
showed that both men and women with dependent children felt more time
pressure than those without, and time spent with pre-school children on
weekdays generally decreased with greater working hours. This suggests
that younger age groups may also experience competition between
engagement in paid work and childcare activities.
Current study
The present study adds to the existing literature by presenting
relatively recent, nationally representative data on the extent of
engagement in multiple productive activities including paid work,
childcare, volunteering, caregiving and domestic work among Australians
aged 15 years and over. We expect that consistent with earlier US and
Australian research findings (Brandon & Temple 2006; Hinterlong
2008), the majority of individuals across all age groups will be engaged
in one or more productive activities.
Engagement in each productive activity is expected to vary by age
and gender, according to dominant social and cultural expectations of
ageing and gender roles in Australia. Consistent with the literature
(Cai & Kalb 2006; McDonald 2010, Nepal et al. 2011b), we expect that
having more human capital and other personal resources such as better
health, higher levels of education and income and being partnered, will
be associated with higher engagement in paid work across all age groups.
The relationship between human capital or personal resources and
engagement in other productive activities is less clear. Prior research
from overseas (Erlinghagen & Hank 2006; Li & Ferraro 2006;
Hinterlong et al. 2007) suggests that the same relationship occurs for
productive activities other than paid work. However, because of the
potential for competition between paid work and other activities, and
the positive association between paid work and human capital, it is
possible that there might be a negative or no relationship between
higher resources and other productive activities.
The current study takes a first step towards examining the
relationship between personal resources and engagement in paid work and
productive activities other than paid work within the Australian
context. The research questions being addressed include 'what is
the extent that Australians are engaged in single and multiple
activities?', 'how are demographic and human capital variables
related to engagement?' and 'how does engagement vary for
different age groups?' Adding to prior research that has typically
focused on one or two specific productive activities, this study
presents data on the rates of engagement in a broader array of
productive activities such as childcare, volunteering, caregiving and
domestic work. These results are disaggregated across all working age
groups by gender, health and education to examine how these factors are
related to engagement over the life course. The study has a particular
focus on individuals in the baby boomer cohort (born 1946-1965) who are
currently under most pressure to continue productive engagement as they
age. We present additional data on engagement for these older age groups
disaggregated by household income, occupation and marital status, and
examine which of these factors are significantly associated with their
engagement in productive activities.
Method
Data and sample
The data were obtained from Wave 10 (2010) of the Household, Income
and Labour Dynamics in Australia (HILDA) Survey. HILDA is a nationally
representative household panel survey conducted annually since 2001 with
an initial Wave 1 sample of 7,682 households (66 per cent response
rate), comprising 19,914 individuals. Within households, only the 15,127
individuals aged 15 years and over were eligible for personal
interviews, resulting in a Wave 1 sample of 13,969 eligible individuals
(92 per cent response rate). At each wave, the sample is extended to
include new household members following changes in household
composition, household members turning 15 years of age and
non-responding Wave 1 household members who decide to participate in a
later wave. Attrition rates across waves are comparable to similar
surveys internationally (Melbourne Institute of Applied Economic and
Social Research 2011). Further details of the sample and survey
methodology have been reported elsewhere (Watson & Wooden 2002).
Wave 10 included data from 13,526 individuals aged 15 to 93. Of
these, 12,048 (89 per cent) returned their Self-Completion
Questionnaire, which contained the time-use questions examined in this
paper. The final sample comprised 10,131 individuals with complete age,
gender, health, educational level and time-use data.
Measures and analysis
Demographic variables including age, gender, health, educational
level, household income, occupation and marital status were obtained in
the personal interview, while time spent on productive activities was
obtained from the Self-Completion Questionnaire (SCQ). In the SCQ,
respondents reported the number of hours they would spend in a typical
week doing paid work, household errands, housework, outdoor tasks,
looking after one's own children, looking after other people's
children, volunteering and caring for a disabled or elderly relative.
Prior research (such as Van der Meet 2006) has often combined
childcare with caregiving to adults or with other forms of instrumental
support that assists others, including housework and household errands.
However, given the possibility that caring for one's own children,
caring for other people's children and caring for a disabled or
elderly relative are likely to show different patterns of engagement
over the lifespan, we kept these three types of caregiving separate.
Time spent on household errands, housework and outdoor tasks was
summed to form a single domestic work variable. Engagement in each
activity was then computed as a dichotomous measure with zero hours
spent on an activity indicating non-involvement and more than zero hours
indicating engagement. However, engagement in domestic work was very
high (above 90 per cent) across all categories of age, gender, health,
education, household income, occupation and marital status, with some
subgroups containing fewer than five individuals not engaged in domestic
work. To improve the reliability of both the domestic work engagement
variable and the chi-square tests, domestic work engagement was divided
into tertiles for a more continuous categorical measure comprising low
(less than 10 hours), mid (10 to 21 hours) and high (more than 21 hours)
domestic work engagement in a typical week.
To provide a relatively informative yet succinct snapshot of how
gender, health and education relate to productive activities over the
life span, respondents were divided into 10-year age groups from 15-24
years of age to 75 years and over. To examine the relationship between
productive engagement and household income, occupation and marital
status specifically for the baby boomer cohort, only respondents aged 45
to 64 were included, divided into 5-year age groups to maximise both
sample size and information on age-graded trends.
Given that health is viewed as a potential enabler or resource for
productive contributions over the life course, a measure of longer-term
functional health was used instead of subjective health ratings, which
can change more frequently following short-term fluctuations in
one's current health status (Bailis et al. 2003; Han et al. 2005).
Respondents who indicated they did not have any long-term health
condition, impairment or disability that restricts them in their
everyday activities were classified as having good health, while those
who did were classified as having a long-term health condition.
(Respondents were presented a list of examples including conditions such
as uncorrected sight, hearing or speech problems, any condition that
restricts physical activity or physical work and any mental illness
which requires help or supervision.)
Consistent with prior research (for example, McDonald 2011), three
levels of education were used: no post-school qualification, post-school
qualification and university degree.
Separate chi-square tests were run for each productive activity
with 10-year age group as the control variable and gender, health or
education entered as the independent variable. In the baby boomer
analysis, separate chi-square tests were run for each productive
activity with 5-year age group as the control variable and income,
occupation or marital status entered as the independent variable.
Finally, to identify which of these factors distinguished between the
baby boomers who engaged in one, multiple or zero activities, separate
multinomial and binomial logistic regressions were run using data from
the 45-64 year-olds.
Results
Sample descriptives for the whole sample and the baby boomer
sub-sample are presented in Table 1. In the total sample, 19.5 per cent
of respondents were aged between 15 and 24, 15.6 per cent aged between
25 and 34, 16.6 per cent aged between 35 and 44, 17.6 per cent aged
between 45 and 54, 14.1 per cent aged between 55 and 64, 9.8 per cent
aged between 65 and 74 and the remaining 6.8 per cent were aged 75 years
and over. Amongst those who indicated engagement in an activity, the
most time was spent in paid work, followed by domestic work, caring for
one's own children, adult caregiving, caring for other
people's children and volunteer work.
Table 2 shows engagement disaggregated by age and gender. The
bottom third of Table 2 shows engagement disaggregated by age group
only. With the exception of the youngest age group who had the highest
engagement in part-time work (34.4 per cent), engagement in full-time
work tended to be lower for older age groups, with a clear decrease for
the 55-64 and 65-74 year-olds relative to the next youngest age group.
Not surprisingly, engagement in caring for one's own children
was relatively high for those aged 25-54, and was highest among 35-44
year-olds (74.6 per cent), whereas engagement in caring for others'
children was generally higher for older age groups, and was highest
among 55-64 year-olds (17.8 per cent). Volunteering and caregiving show
similar trends to engagement in caring for others' children, but
volunteering was highest among 65-74 year-olds (31.9 per cent). Finally,
engagement in domestic work tended to be lower in younger age groups and
higher in the older age groups with the majority of 15-34 year-olds
reporting low engagement in domestic work, while the majority of those
aged 55 and over reported high engagement.
The mean number of activities across all respondents was 2.4 (SD =
1.0). Most respondents reported engaging in at least one activity (98.4
per cent), while 81.5 per cent reported engagement in multiple
activities. Excluding domestic work, which most people engaged in, 82.2
per cent reported engaging in at least one activity, while 40.4 per cent
engaged in multiple activities. Figure 1 displays the percentages of
respondents engaged in zero, single and multiple activities, excluding
domestic work and disaggregated by age and gender. Interestingly, the
majority of younger and older age groups engaged in one or fewer
activities (excluding domestic work), while the majority of those in the
middle age groups (35-54 year-olds) engaged in multiple activities.
Age and gender
Across all ages, males and females showed different rates of
engagement in all productive activities other than caring for one's
own children (see Table 2 column headings). The most obvious gender
difference in engagement across the life course was in paid work. With
the exception of the oldest age groups, engagement in full-time work was
generally lower among females than males, whilst engagement in part-time
work was generally higher.
Females generally reported higher rates of engagement than males in
caring for others' children and volunteering, and were also more
engaged in caregiving for adult relatives from ages 35 to 64. However,
engagement in caring for one's own children was higher for females
than for males only in the 25-44 year age groups, whereas engagement
among the 45-64 year-olds was higher for males than for females,
possibly due to men having children at later ages than women.
To further examine the role of gender in engagement over the life
course, we plotted the average number of hours spent by those engaged in
each activity, disaggregated by age and gender (Figure 2). Consistent
with age-graded norms and the above findings for rates of engagement,
time spent on paid work and caring for one's own children tended to
be higher among younger age groups and lower for older age groups. In
contrast, time spent in other activities tended to be lower for younger
age groups and higher for older age groups. In the age groups between 15
and 64, males tended to spend more time in paid work than in any other
activity, whereas females aged 15-34 spent as much time caring for
one's own children as in paid work. As time spent caring for
one's own children decreases in older age groups for both males and
females, time spent in domestic work and caregiving seems to increase
and is highest in the two oldest age groups. These data highlight the
potential competition for time between different activities and the
variation in time spent in productive engagement across the life course.
Age and health
The data in Table 3 suggest different rates of engagement in all
activities other than volunteering between those with a long-term health
condition and those with good health across all ages. With the exception
of caring for adult relatives and other people's children,
engagement rates were generally higher for those with good health than
those with a long-term health condition. Consistent with the human
capital literature, the most notable difference is the generally higher
engagement in paid work, particularly in full-time paid work, for those
with good health compared to those with a long-term health condition
across all age groups. The relatively higher engagement in volunteering
of those over 65 years with good health compared to those with a
long-term health condition highlights the importance of health for
engagement, particularly at older ages.
Good health was associated with higher engagement in caring for
one's own children among those aged 25-54, and caring for
others' children only for the oldest age group. However, 15-24
year-olds with good health were less engaged in caring for others'
children than those with a long-term health condition. In addition,
among those aged 25-54, engagement in caregiving was higher for those
with poorer health than those with good health.
[FIGURE 2 OMITTED]
Age and education
Consistent with the findings of previous research, Table 4 shows
that higher levels of education tended to be associated with greater
engagement in paid work (either full-time or part-time) and volunteering
across the life course. Engagenaent in caring for one's own
children was also significantly associated with education level. The
data in Table 4 suggest that this is largely due to differences mostly
between those with a degree and those without one. For example, in the
two youngest age groups, only 1.0 per cent and 32.5 per cent of those
with a degree were engaged in caring for one's own children,
compared to 18.5 per cent and 52.2 per cent of those with a non-degree
post-school qualification and 8.9 per cent and 51.1 per cent of those
with no post-school qualification. However, the situation is reversed
among those in middle to later life, with higher education generally
being associated with higher engagement for those over 45 years of age.
A similar pattern was observed for engagement in caring for others'
children and caregiving across the age groups, although the associations
with education were generally not significant.
Baby boomer analysis
Descriptive findings for the baby boomer sample are presented in
Table 1, Column 3. In terms of age, 29.1 per cent of baby boomer
respondents were aged between 45 and 49, 26.5 per cent aged between 50
and 54, 22.8 per cent aged between 55 and 59 and the remaining 21.6 per
cent were aged between 60 and 64.
Age and income
Table 5 presents engagement rates disaggregated by household income
tertiles for the baby boomer cohort aged 45-64 years in 2010. Having a
higher household income was generally associated with higher engagement
in full-time paid work, but lower engagement in part-time paid work,
suggesting that personal income, which is expected to be higher for
those in full-time work, is strongly related to household income. Higher
incomes were also generally associated with higher engagement in caring
for one's own children, although this relationship was not
significant in the 60-64 year age group. While higher incomes were on
average related to lower engagement in caring for others' children
and adult caregiving across age groups, this relationship was
significant only in the 45-49 year group. Finally, although those with
incomes in the highest tertile have the highest engagement in
volunteering, the relationship between income and volunteering was not
significant either within or across age groups.
Age and occupation
The engagement rates for different occupational groups are
displayed in Table 6. Across age groups, occupational group was related
to engagement in paid work, caring for one's own children,
volunteering and caregiving. Managers and professionals generally had
the highest engagement in full-time paid work and caring for one's
own children and the lowest engagement in part-time work. Conversely,
clerical and sales workers generally had the lowest engagement in
full-time paid work and caring for one's own children and the
highest engagement in part-time work. The relationship between
occupation and paid work was significant within all age groups except
for the 60-64 year-olds, while for caring for one's own children,
the relationship was only significant within the 45-49 and 55-59 year
age groups.
The data also suggest that managers, professionals and clerical and
sales workers generally had higher engagement in both volunteering and
caregiving, while machinery operators and labourers generally had lower
engagement across all ages. Trades and community workers in the 45-49
and 50-54 year age groups had similarly low engagement in volunteering
and caregiving, but engagement tended to be higher in the two older age
groups. However, only the relationships between occupation and
volunteering in the 45-59 year age groups were significant. Occupational
group was not related to caring for others' children either across
or within age groups.
Age and partner status
As seen in Table 7, across all ages, those married or in a de facto
relationship generally had higher engagement in paid work, caring for
one's own and others' children and volunteering than those
with no partner. However, these relationships were not significant
within the 60-64 year age group. In addition, marital status was only
significantly related to engagement in caring for one's own
children within the two youngest age groups, and to caring for
others' children in the 55-59 year age group. The results also
suggest no significant relationship between marital status and adult
caregiving either across or within age groups.
Multivariate analysis of baby boomer engagement
The results from the multinomial and logistic regressions examining
which of the above factors distinguish between those engaged in single,
multiple or no activities are shown in Table 8. Domestic work was again
excluded from the activity count due to very high engagement rates,
while occupation, which was only collected from a sub-set of the sample,
was excluded to maximise sample size. Controlling for age, gender,
partner status, education and household income, the odds of being
engaged in one activity or multiple activities rather than no activity
is about three times higher for those in good health compared to those
with a long-term health condition. Other factors that increased the odds
of engagement in a single activity rather than no activity included
being younger, unpartnered and having a higher household income. Being
younger and having a higher household income also increased the odds of
being engaged in multiple activities rather than no activity, as did
being partnered and having a degree.
Paid work engagement was included in the binomial regression to
examine whether it distinguishes between those engaged in single and
multiple activities. Controlling for the other factors in the model, not
being in paid work compared to being in full-time work halved the odds
of being engaged in multiple activities rather than in a single
activity, while being in part-time work increased the odds by 1.48
times. Other significant factors that increased the odds of engagement
in multiple activities were being younger and having a degree.
Discussion
The present study was based on a secondary analysis of recent data
from working-age and older Australians in the nationally representative
HILDA Survey, collected in 2010. The findings update and extend previous
findings from Australia and elsewhere that individuals continue
engagement in productive activities as they age over the life course.
The extent and ways in which people contribute was found to vary with
age and gender, as well as with their social position and personal
resources that can either enable or constrain their capacities and
opportunities.
Overall, the findings are generally consistent with prior research
on age- and gender-related expectations and opportunities within the
Australian context and provide support for the utility of the life
course perspective (see, for example, Elder 1995). The life course
trajectory suggested by the data is that young Australians are likely to
begin their contributions to paid work part-time, when they are also
still studying, before entering the workforce more fully for a period of
time and then having their own children. Those in middle life have
relatively high engagement in all productive activities, but the
contributions shift from paid work and caring for one's own
children towards caring for other people's children or adult
relatives, and towards volunteering in later life.
Our findings also supported those of earlier research on the
influence of gender expectations and opportunities on engagement in paid
work, childrearing and caregiving. Men had relatively higher engagement
than women in paid work, particularly in full-time paid work, and
generally lower engagement in part-time paid work and other activities
beyond paid work. Interestingly, the percentage engaged in caring for
one's own children was higher for males than females in the 45-64
year age groups, suggesting that men have children at older ages than
women. This is possibly related to their higher engagement in paid work,
but is more likely a reflection of the common age gap found in
relationships, with men generally being older, and the different
reproductive timing for men and women. Even so, the mean time spent by
those engaged in caring for one's own children was generally higher
for females than males in all age groups. These mixed results highlight
the value of including different measures of productive engagement and
examining engagement over the life course.
As predicted by human capital theory, the results highlight the
potential for health and education to enable or constrain the types of
activities individuals engage in across the life course. Those with
long-term health conditions and lower educational qualifications
generally had lower engagement than those with good health in all
activities other than caring for other's children and adult
relatives, two activities that are more likely to occur within
one's home or place of residence. These activities are also more
likely to be unpaid, raising concerns about the potential for those with
long-term health conditions and lower educational qualifications to
experience social disadvantage and social exclusion through reduced
opportunities to engage in paid work and other productive activities
across the life course (McDonald 2010).
The additional analyses of the baby boomer cohort aged 45-64 in
2010 suggested that, in addition to the traditional human capital
factors of health and education, personal resources such as having a
higher household income and being partnered were also significantly
related to productive contributions. Those with a partner generally
reported higher engagement in all activities other than adult
caregiving, which was unrelated to marital status. Higher household
incomes were generally associated with higher engagement in full-time
paid work and caring for one's own children, but lower engagement
in part-time work and caring for other people's children or adult
relatives. Being unpartnered and having a lower household income was
also related to decreased odds of engagement, raising concerns about the
likely social and financial disadvantages of those not engaged in
productive activities outside domestic work.
The results for occupation further support the idea that personal
resources can enable or constrain productive engagement. Occupations
that are generally regarded as having higher social and economic status,
such as managerial and professional jobs, were related to higher
engagement in productive activities, while lower status jobs, such as
machinery operators and labourers, tended to be associated with lower
engagement in all activities. Overall, these findings support the
utility of both the human capital and life course frameworks for
research on productive ageing, but they also highlight the added value
of examining other personal resources not typically included in the
human capital framework, such as marital status, household income and
time.
Limitations and future directions
In interpreting the findings it is important to recognise that age
differences may reflect cohort influences as well as life course
influences. For example, educational background and gender expectations,
as well as employment markets, differ considerably for those currently
in early adulthood from those experienced by people now in middle and
later life. Longitudinal analyses are required to understand the impacts
of earlier experiences on the life course and life transitions such as
entries to and exits from paid work and other productive activities
(see, for example, Baxter et al. 2008).
Our future research will extend these findings by examining
transitions over time using several waves of the HILDA Survey. More
in-depth, longitudinal analyses of time use data will add to our
knowledge of the potential competitions or trade-offs in allocating
scarce time between different activities. Future multivariate analyses
will test the relative importance of, and interactions among, enabling
and constraining influences on activities as well as the consequences
for health and wellbeing when people have multiple, demanding
responsibilities such as full-time work and caregiving. Future research
may also examine other potential determinants such as household
structure, number of dependent children, attitudes towards productive
engagement and levels of social capital.
Conclusions and policy implications
Following concerns that governments will be unable to continue
providing adequate pension and healthcare support for an ageing
population as the proportion of those working decreases, governments
have begun implementing strategies including raising pension eligibility
ages, increasing mandatory retirement savings and addressing barriers
such as age discrimination to further encourage and enable older
individuals to remain in the paid workforce longer (OECD 2006; Griffin
et al. 2012; Ryan 2012). The appointment of the first Age Discrimination
Commissioner to the Australian Human Rights Commission in 2011 provided
national leadership in identifying the structural barriers and pervasive
ageism that undermine the recognition and contributions of older people
in the wider community as well as in the workforce. However, our
findings suggest that social and demographic factors such as gender,
human capital and other personal resources may also significantly enable
or constrain engagement in productive activities over the life course
and as individuals age.
Despite the cross-sectional nature of the data limiting our ability
to draw causal inferences, these findings can inform government and
social policies designed to improve the well-being of individuals as
they age and their ability to age productively. For example, current
government actions aimed at improving access to human capital resources
such as health and education, should not only target younger
Australians, but also those in middle and later life, who increasingly
expect and are expected to continue productive engagement as they age.
The clear gender differences found in the data also suggest that there
is great value in promoting policies that reduce gender differences in
paid and unpaid productive contributions, while at the same time
recognising and supporting the contributions of both sexes.
Our findings also underscore the importance of valuing people
across all age groups in terms of their broader contributions beyond
tax-generating economic activities. This is particularly pertinent for
older people, females and those with fewer personal resources, who are
more likely to have reduced engagement in paid work but increased
engagement in unpaid work relative to younger people, males and those
with more personal resources, respectively. However, policy actions
aimed at improving the situation of females and those with fewer
personal resources must not only recognise but also reward unpaid as
well as paid contributions, which can benefit individuals as they age.
The monetary value of unpaid volunteering, childcare and caregiving work
by older Australians has been estimated at approximately $6.8 billion a
year (NSA 2009), providing an important counterpoint to generational
attacks that scapegoat older people for the costs of population ageing
with adverse effects on their social standing and self-esteem (Kendig
2010).
In conclusion, developing policies aimed at reducing social
inequality and increasing human capital and personal resources can help
facilitate the government's productive ageing agenda. More
fundamental action to enable productive economic and social
participation requires a comprehensive view of ageing across the life
course. Our findings suggest that improving education, health,
employment opportunities and the removal of barriers in accessing these
opportunities for those with limited resources can increase
people's capacities for both paid and unpaid contributions across
all ages. Ongoing investments enabling people across the life course to
remain healthy and to have economic security are sound investments in
the future and will be crucial for an increasingly active, engaged and
vital older population.
Acknowledgements
This paper uses unit record data from the Household, Income and
Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was
initiated and is funded by the Australian Government Department of
Families, Housing, Community Services and Indigenous Affairs (FaHCSIA)
and is managed by the Melbourne Institute of Applied Economic and Social
Research (Melbourne Institute). The findings and views reported in this
paper, however, are those of the authors and should not be attributed to
either FaHCSIA or the Melbourne Institute.
This research was conducted by the Australian Research Council
Centre of Excellence in Population Ageing Research (project number
CE110001029). However, the views expressed herein are those of the
authors and are not necessarily those of the Australian Research
Council, who were not involved in decision-making in the research and
publication process.
References
Bailis, D.S., Segall, A. & Chipperfield, J.G. (2003) 'Two
views of self-rated general health status', Social Science and
Medicine, 56 (2), 203-17.
Bass, S.A. & Caro, F.G. (2001) 'Productive aging: a
conceptual framework'. In N. Morrow-Howell, J. Hinterlong & M.
Sherraden (eds) Productive Aging: Concepts and Challenges, Baltimore,
John Hopkins University Press.
Baxter, J., Gray, M. & Hayes, A. (2007) A snapshot of how
Australian families spend their time, AIFS Facts Sheet, Melbourne,
Australian Institute of Family Studies.
Baxter, J., Hewitt, B. & Haynes, M. (2008) 'Life course
transitions and housework: marriage, parenthood, and time on
housework', Journal of Marriage and Family, 70 (2), 259-72.
Bittman, M., Hill, T. & Thomson, C. (2007) 'The impact of
caring on informal carers' employment, income and earnings: a
longitudinal approach', Australian Journal of Social Issues, 42
(2), 255-72.
Brandon, P.D. & Temple, J.B. (2006) 'Determinants of time
allocation combinations among non-employed older persons: evidence from
Australian time use diaries', Australian Journal of Social Issues,
40 (1), 7-26.
Cai, L. & Kalb, G. (2006) 'Health status and labour force
participation: evidence from Australia', Health Economics, 15(3),
241-61.
Carr, A., Biggs, S. & Kimberley, H. (2013) Meanings of a long
life: cultural, social and historical perspectives, Social Policy
Working Paper No. 17, Brotherhood of St Laurence and Centre for Public
Policy, University of Melbourne.
Chomik, R. & Piggott, J. (2012) Mature-age labour force
participation: trends, barriers, incentives, and future potential, CEPAR
Briefing Paper 2012/01, Sydney, Centre of Excellence in Population
Ageing Research.
COTA (Council on the Aging) (2012) Policy alert no.7--Advisory
Panel on the Economic Potential of Senior Australians, COTA, April 2012.
de Vaus, D., Gray M. & Stanton D. (2003) Measuring the value of
unpaid household, caring and voluntary work of older Australians,
Research Paper No. 34, Melbourne, Australian Institute of Family
Studies.
Dosman, D., Fast, J., Chapman, S.A. & Keating, N. (2006)
'Retirement and productive activity in later life', Journal of
Family and Economic Issues, 27 (3), 401-19.
Elder, G.H. (1995) 'The life-course paradigm: social change
and individual development'. In P. Moen, G.H. Elder & K. Lusher
(eds) Examining Lives in Context: Perspectives on the Ecology of Human
Development, Washington, American Psychological Press.
Erlinghagen, M. & Hank, K. (2006) 'The participation of
older Europeans in volunteer work', Ageing and Society, 26 (4),
567-84.
Fernandez-Ballesteros, R., Zamarron, M.D., Diez-Nicolas, J.,
Lopez-Bravo, M.D., Molina, M.A. & Schettini, R. (2011)
'Productivity in old age', Research on Aging, 33 (2), 205-26.
Glass, T., Seeman, T.E., Herzog, A.R., Kahn, R. & Berkman, L.F.
(1995) 'Change in productive activity in late adulthood: MacArthur
studies of successful aging', Journal of Gerontology: Social
Sciences, 50B (2), S65-76.
Griffin, B., Loh, V. & Hesketh, B. (2012) 'Age, gender,
and the retirement process', In M. Wang (ed.), The Oxford Handbook
of Retirement, New York, Oxford University Press.
Han, B., Phillips, C., Ferrucci, L., Bandeen-Roche, K., Jylha, M.,
Kasper, J. & Guralnik, J.M. (2005) 'Change in self-rated health
and mortality among community-dwelling disabled older women', The
Gerontologist, 45 (2), 216-21.
Hesketh, B., Griffin, B. & Lob, V. (2011) 'A
future-oriented Retirement Transition Adjustment Framework',
Journal of Vocational Behavior, 79 (2), 303-14.
Hinterlong, J.E. (2008) 'Productive engagement among older
Americans: prevalence, patterns, and implications for public
policy', Journal of Aging and Social Policy, 20 (2), 141-64.
Hinterlong, J., Morrow-Howell, N. & Rozario, P.A. (2007)
'Productive engagement and late life physical and mental health:
findings from a nationally representative panel study, Research on
Aging, 29 (4), 348-70.
Jenkins, A., Rowland, F., Angus, P. & Hales, C. (2003) The
Future Supply of Informal Care 2003 to 2013: Alternative Scenarios,
Australian Institute of Health and Welfare (AIHW) Cat. No. AGE 32,
Canberra, AIHW.
Kendig, H. (2010) 'The Intergenerational Report 2010: a
double-edged sword', Australasian Journal on Ageing, 29 (4),
145-46.
Kendig, H. & Browning, C. (2010) 'A social view on healthy
ageing: multi-disciplinary perspectives and Australian evidence'.
In D. Dannefer & C. Phillipson (eds) The SAGE Handbook of Social
Gerontology, London, Sage Publications.
Kendig, H., Dykstra, P.A., van Gaalen, R.I. & Melkas, T. (2007)
'Health of aging parents and childless individuals', Journal
of Family Issues, 28 (11), 1457-86.
Kendig, H., Wells, Y., O'Loughlin, K. & Heese, K. (in
press) 'Australian baby boomers face retirement during the Global
Financial Crisis', Journal of Aging and Social Policy.
Kjellsson, S. (2013) 'Accumulated occupational class and
self-rated health. Can information on previous experience of class
further our understanding of the social gradient in health?',
Social Science and Medicine, 81, 26-33.
Li, Y. & Ferraro, K. F. (2006) 'Volunteering in middle and
later life: is health a benefit, barrier or both?' Social Forces,
85 (1), 497-519.
Mathers, C. (2007) 'The health of older Australians', In
A. Borowski, S. Encel & E. Ozanne, (eds) Longevity and Social Change
in Australia, Sydney, UNSW Press.
McDonald, I. (2010) 'Economic perspective on social
exclusion', Australian Journal of Social Issues, 45 (2), 213-25.
McDonald, P. (2011) 'Employment at older ages in Australia:
determinants and trends'. In T. Griffin & F. Beddie (eds) Older
Workers: Research Readings, Adelaide, Commonwealth of Australia.
McMunn, A, Nazroo, J., Wahrendorf, M., Breeze, E. & Zaninotto,
P. (2009) 'Participation in socially-productive activities,
reciprocity and wellbeing in later life: baseline results in
England', Ageing and Society, 29 (5), 765-82.
Melbourne Institute of Applied Economic and Social Research (2011)
HILDA Survey Annual Report 2010, Melbourne Institute of Applied Economic
and Social Research, University of Melbourne.
Mirowsky, J. & Ross, C.E. (1998) 'Education, personal
control, lifestyle and health: a human capital hypothesis',
Research on Aging, 20 (4), 415-49.
Moen, P. (1996) 'A life course perspective on retirement,
gender and well-being', Journal of Occupational Health Psychology,
1 (2), 131-44.
Moen, P., & Spencer, D. (2006) 'Converging divergences in
age, gender, health, and well-being: strategic selection in the third
age.' In R. H. Binstock & L. K. George (eds) Handbook of Aging
and the Social Sciences, Amsterdam, Elsevier.
Mor, V. (2005), 'The compression of morbidity hypothesis: a
review of research and prospects for the future', Journal of the
American Geriatrics Society, 53 (9s), S308-S309.
Morrow-Howell, N., Hinterlong, J. & Sherraden, M. (eds) (2001a)
Productive Aging: Concepts and Challenges. Baltimore, Johns Hopkins
University Press.
Morrow-Howell, N., Hinterlong, J., Sherraden, M. & Rozario, P.
(2001b) 'Advancing research on productivity in later life'. In
N. Morrow-Howell, J. Hinterlong & M. Sherraden (eds) Productive
Aging: Concepts and Challenges, Baltimore, John Hopkins University
Press.
Nepal, B., Brown, L., Kelly, S., Percival, R., Anderson, P.,
Hancock, R. & Ranmuthugala, G. (2011a) Projecting the need for
formal and informal aged care in Australia: a dynamic microsimulation
approach, National Centre for Social and Economic Modelling (NATSEM)
Working Paper 11/07, Canberra, NATSEM, University of Canberra.
Nepal, B., Brown, L., Ranmuthugala, G. & Percival, R. (2011b)
'A comparison of the lifetime economic prospects of women informal
carers and non-carers, Australia, 2007', Australian Journal of
Social Issues, 46 (1) 91-108.
NSA (National Seniors Australia) (2009) Still Putting In: Measuring
the Economic and Social Contributions of Older Australians, Research
Report, Canberra, National Seniors Australia Productive Ageing Centre,
Department of Health and Ageing.
--(2012) Barriers to Mature Age Employment: Final Report of the
Consultative Forum on Mature Age Participation, Canberra, National
Seniors Australia Productive Ageing Centre, Department of Health and
Ageing.
OECD (Organisation for Economic Co-operation and Development)
(2006) Live Longer, Work Longer, Paris, Organisation for Economic
Co-operation and Development.
--(2011) Pensions at a Glance 2011: Retirement-income Systems in
OECD and G20 Countries, Paris, Organisation for Economic Co-operation
and Development.
O'Loughlin, K., Humpel, N. & Kendig, H. (2010)
'Impact of the global financial crisis on employed Australian baby
boomers: a national survey', Australasian Journal on Ageing, 29
(2), 89-91.
O'Reilly, P. & Caro, F.G. (1994) 'Productive ageing:
an overview of the literature', Journal of Aging and Social Policy,
6 (3), 39-71.
Ryan, S. (2012) Working Past Our 60s: Reforming Laws and Policies
for the Older Worker 2012, Sydney, Australian Human Rights Commission.
Shomos, A. (2010) Links Between Literacy and Numeracy Skills and
Labour Market Outcomes, Productivity Commission Staff Working Paper,
Melbourne, Productivity Commission.
Tang, F., Morrow-Howell, N. & Choi, E. (2010) 'Why do
older adult volunteers stop volunteering?', Ageing and Society, 30
(5), 859-78.
Treasury (2010) Australia to 2050: Future Challenges,
Intergenerational Report (IGR) 2010, Canberra, Commonwealth of
Australia.
--(2011) Realising the Economic Potential of Senior Australians:
Turning Grey into Gold, Advisory Panel on the Economic Potential of
Senior Australians (EPSA) Report 3, Canberra, Commonwealth of Australia.
--(2012) Government Response to the Final Report of the Advisory
Panel on the Economic Potential of Senior Australians, Canberra,
Commonwealth of Australia.
Van der Meer, M. (2006) 'Productivity among older people in
the Netherlands: variations by gender and the socio-spatial context in
2002-03', Aging and Society, 26 (6), 901-23.
Walker, A. (2008) 'Commentary: The emergence and application
of active aging in Europe', Journal of Aging and Social Policy, 21
(1), 75-93.
Watson, N. & Wooden, M. (2002) The Household, Income and Labour
Dynamics in Australia (HILDA) Survey: Wave 1 Survey Methodology, HILDA
Project Technical Paper Series No. 1/02, Melbourne Institute of Applied
Economic and Social Research, University of Melbourne.
WHO (World Health Organization) (2010) Mental Health and
Development: Targeting People with Mental Health Conditions as a
Vulnerable Group, Geneva, World Health Organization.
Table 1: Participant characteristics of the
whole sample and of the baby boomers only
Baby
All boomers
n 10,131 3,208
Female (%) 52.7 51.9
Married or de facto (%) 62.8 74.6
Dependent child/children (%) 40.4 32.7
Education (%)
Degree qualification 22.8 26.7
Non-degree qualification 30.7 36.3
No post-school qualification 46.5 37.1
Paid work (%) 62.8 70.7
Mean hours per week 35.9 (15.4) 38.0 (14.5)
Own childcare (%) 33.3 35.1
Mean hours per week 18.1 (19.9) 9.7 (10.9)
Other childcare (%) 12.0 13.7
Mean hours per week 7.4 (11.1) 9.3 (12.7)
Volunteer work (%) 20.3 24.4
Mean hours per week 5.0 (6.8) 5.2 (7.6)
Caregiving (%) 9.3 16.5
Mean hours per week 16.1 (26.0) 14.8 (25.7)
Domestic work (%) 97.6 99.3
Mean hours per week 19.4 (16.6) 21.9 (16.3)
Mean hours is for those engaged in each
activity. Standard deviations are in parentheses.
Table 2: Percentage engaged in each productive
activity by age and gender
Paid work Childcare
***
FT PT Own Other ***
Females (n = 5,341)
15-24 25.3 *** 40.0 11.0 11.9 **
25-34 46.3 *** 27.4 48.6 *** 11.7 **
35-44 35.4 *** 36.5 78.2 *** 16.8 ***
45-54 391 *** 36.6 43.6 *** 12.3 **
55-64 25.2 *** 22.9 10.2 *** 21.7 ***
65-74 1.8 ** 7.5 5.9 18.8 **
75 and over 0.0 2.6 2.1 5.5
Males (n = 4,790)
15-24 37.6 28.0 8.7 7.7
25-34 83.7 7.8 40.2 7.5
35-44 85.9 4.9 70.4 11.1
45-54 79.1 8.0 59.0 8.4
55-64 51.3 16.7 20.4 13.4
65-74 5.7 8.8 7.4 11.9
75 and over 0.3 3.3 4.0 5.3
All (n = 10,131)
15-24 31.1 34.4 10.0 9.9
25-34 63.7 18.3 44.7 9.7
35-44 59.2 21.6 74.6 14.1
45-54 58.5 22.7 51.1 10.4
55-64 37.6 19.9 15.0 17.8
65-74 3.7 8.2 6.6 15.4
75 and over 0.1 2.9 2.9 5.4
Volunteer Caregiving Domestic work
*** ***
work *** Low Mid High
Females (n = 5,341)
15-24 11.2 2.7 68.4 *** 24.2 7.4
25-34 16.5 ** 2.7 30.8 *** 34.3 34.9
35-44 26.6 *** 9.8 *** 11.0 *** 32.2 56.8
45-54 25.0 * 18.8 *** 13.8 *** 36.1 50.1
55-64 26.8 21.3 *** 12.5 *** 32.8 54.7
65-74 34.9 * 11.7 7.5 *** 22.6 69.9
75 and over 21.7 9.2 20.9 28.0 51.0
Males (n = 4,790)
15-24 9.8 3.1 76.5 18.8 4.7
25-34 11.0 3.5 51.2 35.4 13.4
35-44 17.5 5.2 36.9 43.1 20.0
45-54 20.8 12.3 32.3 45.3 22.4
55-64 25.4 13.6 29.2 41.3 29.5
65-74 28.9 10.5 19.3 33.8 46.9
75 and over 22.2 11.3 21.9 35.1 43.0
All (n = 10,131)
15-24 10.6 2.9 72.2 21.6 6.2
25-34 14.0 3.1 40.3 34.8 24.9
35-44 22.3 7.6 23.2 37.4 39.4
45-54 23.0 15.6 22.8 40.6 36.7
55-64 26.1 17.6 20.4 36.9 42.7
65-74 31.9 11.1 13.3 28.1 58.6
75 and over 21.9 10.1 21.3 31.1 47.5
N = 10,131. FT = Full-time (35 hours or more in a typical week);
PT = Part-time; Domestic work tertiles: Low = Less than 10 hours;
Mid = 10 to 21 hours; High = More than 21 hours.
* p < .05; ** p < 01; *** p < .001
Table 3: Percentage engaged in each
productive activity by age and health
Paid work Childcare
***
FT PT Own *** Other *
Long-term health condition (n = 2,720)
15-24 23.3 *** 29.6 10.9 16.7 ***
25-34 44.3 *** 23.5 37.7 * 12.6
35-44 42.9 *** 20.7 65.8 *** 14.4
45-54 36.6 *** 20.9 42.3 *** 11.9
55-64 24.8 *** 16.0 13.7 19.2
65-74 2.1 *** 5.5 6.9 13.7
75 and over 0.0 2.1 2.5 3.2 **
Good health (n = 7,411)
15-24 32.2 35.1 9.8 8.9
25-34 66.2 17.6 45.6 9.4
35-44 63.0 21.8 76.6 14.0
45-54 66.5 23.4 54.3 9.9
55-64 46.2 22.6 15.9 16.8
65-74 5.2 10.6 6.4 17.0
75 and over 0.4 4.5 3.7 9.4
Volunteer Caregiving Domestic work
*** ***
work Low Mid High
Long-term health condition (n = 2,720)
15-24 12.8 3.9 70.4 21.4 8.2
25-34 13.7 8.7 *** 45.4 31.1 23.5
35-44 18.8 14.1 *** 27.6 ** 30.1 42.3
45-54 19.7 * 18.6 * 22.6 ** 34.9 42.5
55-64 24.8 18.8 19.2 37.1 43.8
65-74 25.5 *** 11.2 15.2 26.5 58.3
75 and over 17.5 *** 9.6 25.1 *** 33.0 41.9
Good health (n = 7,411)
15-24 10.2 2.7 72.5 21.7 5.9
25-34 14.0 2.4 39.6 35.3 25.1
35-44 23.1 6.1 22.2 39.1 38.8
45-54 24.2 14.5 22.8 42.6 34.5
55-64 27.0 16.8 21.3 36.7 42.0
65-74 37.8 11.0 11.6 29.5 58.9
75 and over 29.8 11.0 14.7 27.8 57.6
N = 10,131. FT= Full-time (35 hours or more in a typical week);
PT = Part-time; Domestic work tertiles. Low = Less than 10 hours;
Mid = 10 to 21 hours; High = More than 21 hours.
* p < .05; ** p < .01; *** p < .001
Table 4: Percentage engaged in each
productive activity by age and education
Paid work Childcare Volunteer
***
FT PT Own *** Other work ***
Degree (n = 2,311)
15-24 72.3 *** 16.8 1.0 *** 5.0 13.9
25-34 69.1 *** 18.9 32.5 *** 8.2 16.8 *
35-44 62.8 *** 25.5 75.5 13.7 28.0 ***
45-54 66.1 *** 22.1 59.4 *** 7.8 33.9 ***
55-64 44.5 *** 258 21.6 *** 16.8 34.5 ***
65-74 6.8 *** 14.9 6.2 17.4 47.8 ***
75 and over 1.4 *** 11.1 12.5 *** 11.1 * 33.3 *
Non-degree post-school
qualification (n = 3,106)
15-24 56.9 24.9 18.5 9.4 7.1
25-34 62.9 20.6 52.2 11.8 13.8
35-44 64.6 18.5 75.5 13.3 20.0
45-54 61.6 22.9 50.1 11.8 20.6
55-64 43.5 18.5 13.8 16.3 25.5
65-74 3.7 7.6 5.2 15.6 31.5
75 and over 0.0 2.2 1.7 3.4 23-0
No post-school
qualification (n = 4,714)
15-24 23.6 37.3 8.9 10.3 11.0
25-34 57.8 14.7 51.1 9.3 10.6
35-44 49.6 21.1 72.6 15.4 19.1
45-54 48.4 22.9 45.2 11.0 16.5
55-64 28.9 17.6 12.1 19.5 21.6
65-74 2.8 6.3 7.7 14.7 27.1
75 and over 0.0 1.8 1.8 5.3 19.6
Caregiving Domestic work
* ***
Low Mid High
Degree (n = 2,311)
15-24 1.0 62.4 *** 31.7 5.9
25-34 2.6 41.8 ** 37.7 20.5
35-44 4.8 ** 21.1 40.8 38.1
45-54 18.9 20.7 * 45.6 33.7
55-64 19.3 22.4 *** 40.9 36.7
65-74 12.4 8.7 33.5 57.8
75 and over 12.5 23.6 30.6 45.8
Non-degree post-school
qualification (n = 3,106)
15-24 4.4 47.2 47.2 47.2
25-34 3.3 47.2 47.2 47.2
35-44 9.2 47.2 47.2 47.2
45-54 14.3 47.2 47.2 47.2
55-64 15.9 47.2 47.2 47.2
65-74 13.5 47.2 47.2 47.2
75 and over 5.6 47.2 472 47.2
No post-school
qualification (n = 4,714)
15-24 2.7 75.5 18.8 5.7
25-34 3.5 42.0 28.6 29.4
35-44 8.6 24.4 34.2 41.4
45-54 14.5 22.8 36.8 40.5
55-64 18.0 19.3 31.5 49.2
65-74 9.1 14.5 25.9 59.6
75 and over 11.5 23.0 29.0 47.9
N = 10,131. FT = Full-time (35 hours or more in a typical week);
PT = Part-time. Domestic work tertiles: Low = Less than 10 hours;
Mid -10 to 21 hours; High = More than 21 hours.
* p < .05; ** p < .01; *** p < .001
Table 5: Percentage engaged in each productive
activity by age and household income
Paid work Childcare
***
FT PT Own *** Other **
Under $60,000 (n = 1,018)
45-49 35.9 *** 25.2 51.3 *** 12.0 **
50-54 28.2 *** 24.1 27.7 *** 14.4
55-59 17.9 *** 21.4 12.7 *** 18.3
60-64 13.6 *** 19.2 10.0 18.6
$60,000 - $124,999 (n = 1,200)
45-49 65.8 21.9 57.0 11.7
50-54 59.7 24.6 42.9 11.8
55-59 57.8 20.4 20.4 18.3
60-64 34.5 24.6 7.9 17.7
$125,000 and over (n = 917)
45-49 71.2 23.0 69.0 8.6
50-54 75.0 18.9 51.1 5.0
55-59 69.8 15.6 28.1 15.1
60-64 51.8 17.9 12.5 20.5
Volunteer Caregiving Domestic work
** ***
work Low Mid High
Under $60,000 (n = 1,018)
45-49 23.9 22.2 *** 27.8 34.6 37.6
50-54 15.9 19.5 21.5 * 37.4 41.0
55-59 23.1 21.0 17.9 ** 34.9 47.2
60-64 28.1 17.5 14.4 ** 31.7 53.9
$60,000 - $124,999 (n = 1,200)
45-49 22.8 12.0 23.6 40.5 35.9
50-54 20.2 14.8 26.1 38.4 35.6
55-59 25.6 17.0 21.5 46.4 32.2
60-64 24.6 16.3 23.2 37.9 38.9
$125,000 and over (n = 917)
45-49 29.1 10.7 18.7 43.3 38.0
50-54 23.2 17.9 18.9 49.6 31.4
55-59 27.1 19.1 26.6 37.7 35.7
60-64 29.5 12.5 24.1 31.3 44.6
N = 3,135. FT = Full-time (35 hours or more in a typical week);
PT = Part-time. Domestic work tertiles: Low = Less than 10 hours;
Mid = 10 to 21 hours; High = More than 21 hours. Household
income was divided into tertiles according to gross income band
for the last financial year.
* p < .05: ** p < .01; *** p < .001
Table 6: Percentage engaged in each
productive activity by age and occupation
Paid work *** Childcare Volunteer
FT PT Own *** Other work ***
Managers and professionals (n = 991)
45-49 78.0 *** 20.1 64.4 * 10.8 31.9 **
50-54 75.0 ** 20.9 49.3 6.8 28.4 ***
55-59 78.4 ** 18.8 28.6 ** 11.3 30.5 *
60-64 48.4 39.6 11.3 14.5 25.8
Trades and community workers (n = 482)
45-49 67.7 30.6 64.5 9.1 18.8
50-54 70.4 28.2 40.1 12.7 10.6
55-59 57.0 36.6 23.7 22.6 24.7
60-64 57.4 31.1 11.5 115 29.5
Clerical and sales workers (n = 531)
45-49 62.5 35.6 52.5 8.1 25.6
50-54 58.1 35.6 38.8 10.0 21.9
55-59 63.8 29.9 11.8 18.1 22.0
60-64 51.2 40.5 4.8 17.9 250
Machinery operators and labourers (n = 347)
45-49 63.5 27.8 57.4 12.2 17.4
50-54 66.3 28.7 50.5 12.9 11.9
55-59 63.8 28.8 22.5 15.0 13.8
60-64 51.0 39.2 13.7 7.8 98
Caregiving Domestic work
**
Low Mid High
Managers and professionals (n = 991)
45-49 13.6 25.4 44.3 30.3
50-54 20.3 22.6 47.0 30.4
55-59 21.1 26.8 47.9 25.4
60-64 14.5 23.9 428 33.3
Trades and community workers (n = 482)
45-49 15.1 24.2 37.6 38.2
50-54 12.0 22.5 45.1 32.4
55-59 20.4 23.7 37.6 38.7
60-64 11.5 27.9 44.3 27.9
Clerical and sales workers (n = 531)
45-49 10.6 19.4 46.9 33.8
50-54 18.1 25.6 40.6 33.8
55-59 18.1 22.8 45.7 31.5
60-64 22.6 19.0 310 50.0
Machinery operators and labourers (n = 347)
45-49 8.7 27.8 36.5 35.7
50-54 10.9 25.7 38.6 35.6
55-59 8.8 27.5 43.8 28.8
60-64 15.7 31.4 35.3 33.3
N = 2,351. FT = Full-time (35 hours or more in a typical week);
PT = Part-time. Domestic work tertiles: Low = Less than 10 hours;
Mid = 10 to 21 hours; High = More than 21 hours.
* p < 05; ** p < .01, *** p < .001
Table 7: Percentage engaged in each productive
activity by age and marital status
Paid work *** Childcare
FT Pr Own *** Other **
Married or de facto (n = 2,391)
45-49 60.8 *** 25.0 67.3 *** 10.4
50-54 59.1 *** 24.0 46.8 *** 10.8
55-59 49.9 * 20.4 21.3 19.8 ***
60-64 27.3 20.1 9.1 19.9
No partner (n = 812)
45-49 57.7 17.0 38.2 11.6
50-54 50.0 18.2 27.3 8.2
55-59 43.2 15.9 15.3 8.5
60-64 23.4 22.3 12.6 14.3
Volunteer Caregiving Domestic work *
work *** Low Mid High
Married or de facto (n = 2,391)
45-49 27.5 * 13.6 21.7 * 38.2 40.1
50-54 22.3 * 16.7 21.5 41.9 36.6
55-59 27.4 * 19.5 22.7 39.8 37.5
60-64 26.9 16.8 18.6 31.9 49.5
No partner (n = 812)
45-49 19.9 15.8 26.6 44.0 29.5
50-54 14.5 18.6 25.5 40.9 33.6
55-59 18.8 18.2 18.8 42.0 39.2
60-64 27.4 13.7 20.6 36.6 42.9
N = 3,203. FT = Full-time (35 hours or more in atypical week);
PT = Part-time. Domestic work tertiles: Low = Less than 10
hours; Mid = 10 to 21 hours; High = More than 21 hours.
* p < .05; ** p < .01; *** p < .001
Table 8: Multinomial (no activity vs. single/multiple activities)
and binomial (single vs. multiple activities) logistic regressions,
45-64 year olds
No activity vs.
single activity
B ExpB 95% CI
Age -.04 .96 * .94 .98
Gender (0 = Males) -.22 .80 .62 1.04
Good health 1 09 2.98 * 2.30 3.86
Married or de facto -.37 .69 .52 .91
Degree vs. No post-school -.32 .73 .50 1.05
qualification
Degree vs. Non-degree -.06 .94 .64 1.39
post-school qualification
High vs. low household income -1.57 .96 *** .94 .98
High vs. mid household income -.72 .80 .62 1.04
Full-time vs. no paid work -- -- -- --
Full-time vs. part-time work -- -- -- --
N = 3,180; Nagelkerke R2 = 225;
[chi square](16) = 677.03 ***
No activity vs.
multiple activities
B ExpB 95% CI
Age -.12 .89 *** .87 .91
Gender (0 = Males) -.36 .70 *** .54 .91
Good health 1.16 3.19 *** 2.45 4.15
Married or de facto .39 1.48 ** 1.10 1.98
Degree vs. No post-school -1.00 .37 *** .25 .53
qualification
Degree vs. Non-degree -.51 .60 ** .41 .88
post-school qualification
High vs. low household income -1 62 .20 *** .12 .31
High vs. mid household income -.85 .43 *** .27 .68
Full-time vs. no paid work -- -- -- --
Full-time vs. part-time work -- -- -- --
Single vs.
multiple activities
B ExpB 95% CI
Age -.08 .93 *** .91 .94
Gender (0 = Males) -.14 .87 .73 1.03
Good health -.05 .95 .79 1.14
Married or de facto .79 2.21 *** 1.80 2.70
Degree vs. No post-school -.64 .53 *** .43 .65
qualification
Degree vs. Non-degree -.47 .63 *** .51 .77
post-school qualification
High vs. low household income .10 1.11 .36 1.42
High vs. mid household income -.13 .88 .72 1.06
Full-time vs. no paid work -.69 .50 *** .40 .64
Full-time vs. part-time work .39 1.48 *** 1.20 1.83
N = 2,816; Nagelkerke R2 = .152;
[chi square](10) = 338.75 ***
ExpB refers to change in odds ratio with one unit change in the
predictor. Household income tertiles: Low = Under $60,000;
Mid = $60,000 to $124,999; High = $125,000 and over.
* p < .05; ** p < .01; *** p < .001
Figure 1: Percentage engaged in single, multiple and no activities
by age and gender
Males
Percentage (%) engaged
Age group
(years) No Activities One Activity Multiple activities
15-24 26.6 55.7 17.7
25-34 4.2 48.9 46.9
35-44 3.4 23.4 73.2
45-54 4.6 30.3 65.1
55-64 15.2 45.6 39.2
65-74 44.5 41.0 14.5
75+ 60.6 33.8 5.6
Females
Percentage (%) engaged
Age group
(years) No Activities One Activity Multiple activities
15-24 20.2 61.1 18.7
25-34 3.1 52.9 44.0
35-44 3.1 25.9 70.9
45-54 9.2 35.3 55.5
55-64 19.6 44.6 35.7
65-74 40.0 42.8 17.2
75+ 66.0 29.3 4.7
Note: Table made from bar graph.
