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A comparison of the lifetime economic prospects of women informal carers and non-carers, Australia, 2007.


Informal care provided at home is the backbone of the disability and aged care system in Australia. The Australian Bureau of Statistics (ABS 2009) estimated that, in 2003, 2.6 million persons aged 15 years and over provided care to a family member who needed assistance due to disability, long-term health condition, old-age or frailty. In 2005, informal carers were estimated to have provided a total of 2.5 billion hours of care, valued at between $4.9 billion and $30.5 billion, which works out to be between 0.6 and 3.5 per cent of gross domestic product (GDP) (Access Economics 2005). With the ageing of the population, the need for informal care is likely to increase further (Percival & Kelly 2004) and hence have economic implications as well.

While it has been recognised that integrated and well-coordinated homebased care provided through family members is an efficient and cost-effective model of caring for the frail, elderly individuals or persons with a disability (Peters & Sellick 2006; Hollander et al. 2007), the economic implications of this model in Australia have yet to be fully examined. Recently, concerns have been raised from within the Government that the current system of heavy reliance on informal care is unsustainable and that a life course perspective is to be adopted to ensure wellbeing of people with a disability and their families (Disability Investment Group 2009). This view encourages taking a lifetime perspective while examining the economic, social and health implications of a caring role. With evidence suggesting that informal carers are experiencing significant financial and psychological burdens (Zapart et al. 2007; Edwards et al. 2008; Gray et al. 2008), there is a need to further investigate how these burdens would manifest over their lifetime. This paper aims to investigate the economic dimension of this important question.

Focusing on the economic aspects, this study examines the earnings prospects of women over their working life between 30 and 64 years of age, comparing women who are primary carers with women who are not carers. The analysis is limited to women as they are the majority of carers (ABS 2009) and women are more likely to be financially vulnerable than men because of poorer labour force participation and lower level of earnings over the lifetime (Cassells et al. 2009). When matched on the basis of their education, health and partnership status, carers who are financially most disadvantaged are identified. Implications of this income gap on saving towards retirement are also discussed. The findings of the study are expected to help identify the subgroups of carers who are financially disadvantaged, and for whom policies should be targeted to assist in the opportunity cost of caring.

An Overview of Previous Research

Informal carers, those who are primarily responsible for the care (in the home) of an elderly and frail person, or a person with a long-term health condition or a disability, tend to have reduced or forgone employment and hence incur substantial financial consequences. This has been reported internationally as well as in Australia (Stommel et al. 1994; Emanuel et al. 1999; Lukemeyer et al. 2000; Anderson 2004; Access Economics 2005). Previous research in Australia provides evidence of a substantial gap between women informal carers and other women with regard to educational, health and economic outcomes. Compared to women in general (or non-carers), women primary carets are found to have a lower level of educational qualifications, poorer health status, reduced workforce participation, and reduced earnings (Cummins et al. 2007; Edwards et al. 2008; Gray et al. 2008).

Women informal carers are themselves experiencing poor health (Zapart et al. 2007; Edwards et al. 2008). A greater proportion of carers than other women tend to have poorer self-assessed health (Nepal et al. 2008) and their poor health is largely attributed to their lower level of mental health (Zapart et al. 2007). As Edwards et al. (2008) note, carers tend to report having higher levels of depression and stress, and lower levels of general subjective wellbeing than do non-carets. Even among carers, female caters are found to have lower levels of wellbeing than do male carers (Cummins et al. 2007).

The nature of the caring role is time consuming, stressful and hence many carets forgo their job or reduce their working hours to fulfil their caring responsibilities. The Australian Government, through Centrelink, financially supports carers by providing Carer Payment (an income and assets tested social security payment) to the persons who are unable to support themselves through substantial paid employment owing to their caring responsibilities and/or Caret Allowance (an income supplement which is not income and assets tested). A 2006 study of a sample of carers drawn from the Centrelink administrative database showed that labour force participation rates were 30.6 per cent for women carets receiving Carer Payment and 53.7 per cent for those receiving Carer Allowance only (Edwards et al. 2008). These rates were lower than the labour force participation rate of 57 per cent among women of working age in general (ABS 2006a). While some primary caters strive to maintain a full-time job, most are unable to work full-time. Edwards et al. (2008) found that almost half of women carers who were outside the labour force at the time of the study were working immediately prior to assuming the carer role. They also found that a majority (58.8 per cent) of employed carers receiving Carer Payment and nearly two-fifths (39.3 per cent) of those receiving Carer Allowance had to give up their job at some point or other to meet the demand of their caring role. Likewise, two thirds (66.7 per cent) of employed women carers receiving Carer Allowance and 58.8 per cent of those receiving Carer Payment took leave to fulfil their caring role. It is therefore not surprising that compared to the 28.8 per cent full-time employment rate for Australian women in 2004 (ABS 2006b), only 0.8 per cent of Caret Payment recipients and 11.4 per cent of Cater Allowance only recipients worked full-time (Edwards et al. 2008, Table 11.1).

This foregone employment results in a lower income, which, in turn, often leads to financial stress. It has been found that caters are twice as likely as people in general to worry about the inadequacy of their income to meet their household expenses (Cummins et al. 2007). Edwards et al. (2008) report that around 30 per cent of families receiving carer benefits experience difficulty in paying utility bills as compared to 14.6 per cent of the general population. This may be one of several instances of financial stress carers experience as their average household incomes tend to be much lower than those of the general population (Cummins et al. 2007).

Conceptual Framework

In this study, the employment status and earnings over the working life of women primary carers are compared with those of women non-carers by disaggregating the results by level of education, health status and partnership status. The purpose of the research and the choice of the factors considered in this study are guided by the concept of human capital.

There has been a growing focus on people's education and health as the vehicle for personal and social prosperity. This idea has been established through the literature in human capital and broadened by the interrelated concept of human capability or human development (Sen 1997). The concept of human capital identifies education, training and health as important factors enhancing productivity and hence future earnings (Schultz 1961; Becker 1962; Becker 2006). This concept highlights the fact that the investment in people has economic benefits to the society as a whole. Education boosts sense of personal control, physical functioning, self-assessed health, productivity and lifetime earnings (Schultz 1961; Becker 1962; Mirowsky & Ross 1998; Danziger et al. 2000). Education and health, along with earnings, are now firmly established as the major dimensions of human development internationally (United Nations Development Program 2009). Studies examining lifetime earnings have established that educational differences in earnings are substantial (Wei 2001; Day & Newburger 2002). This fact is supported by recent findings from Australia that the level of education has important implications for women's labour force participation and earning capacity (for example, Wei 2001; Cassells et al. 2009). Therefore, we have differentiated our analyses by broad levels of educational achievements by the study population.

Likewise, both employment and earnings of individuals are found to be strongly associated with health status (Stronks et al. 1997; Walker et al. 2003; Nepal et al. 2009). Therefore, we have included health as an important explanatory factor in our analysis. There was also a policy related reason to consider health status in this analysis. While shifting educational levels amongst the population requires medium to long-term policy initiatives, changes in health status would be amenable to short-term interventions such as the provision of occasional respite services to boost psychological relief for carers.

In addition to education and health, we have considered partnership status in this analysis, acknowledging that the availability of financial resources for the present as well as for retired life are likely to be higher for women who are partnered. Research suggests that single motherhood is likely to compromise labour force participation and earning potential, and hence single mother families are likely to experience higher poverty rates (Wong et al. 1993; Nichols-Casebolt & Krysik 1997). An additional reason to derive separate estimates for sole mothers and partnered mothers was the need to distinguish between families where the primary carer is the only able bodied working-age adult and those where other able-bodied adults are present (Edwards et al. 2008).



This study used the 2007 Household, Income and Labour Dynamics in Australia (HILDA) Survey--Wave 7 to model employment and financial outcomes over the working life of women primary carers and non-carers. The HILDA survey collects data from a representative sample of Australian residents living in private dwellings (but those living in remote and sparsely populated areas are excluded) (Watson & Wooden 2001). This data set is a rich source of information on demographic, educational and health status and also identifies primary carers and non-caters. Partners' characteristics can also be identified in this data set.

The HILDA Survey identifies carers by asking the following question:
   Is there anyone in this household, who has a long-term health
   condition, who is elderly or who has a disability, and for who
   you care or help on an ongoing basis with any of the types of
   activities listed on SHOWCARD K7?"

The SHOWCARD K7 lists activities related to self-care, mobility and communication in own language (see Appendix).

Primary carers are identified by the follow-up question:
   Are you the main carer of [this person/any of these people]?
   (That is, are you the person who provides most of their care?)

Study Population

This study focuses on women primary carers aged 30 to 64 years. Women constitute the majority of carets in Australia. In 2003, women comprised 54 per cent all carers who provided assistance to someone needing help due to disability or age, and 71 per cent of primary caters (ABS 2004). This skewed gender ratio in the caret population means that women are disproportionately taking up the burden of performing a carer role (Jenson & Jacobzone 2002). This is likely to widen the gender inequality in earning capacity (Briggs et al. 2006). Women not only constitute a greater proportion of the caret population, they also experience a lower level of general wellbeing compared to men (Cummins et al. 2007).

Table 1 presents selected basic socio-demographic characteristics of women primary carers and non-carers, aged 30 to 64 years. The right hand column presents the ratio of per cent distributions of primary carets to non-carets. Overall, women primary caters are older than non-carers. The proportion of women who are primary carers increases with age while that female non-carers tends to decline. In terms of partnership patterns, the proportion of partnered mothers is slightly lower among primary carers (44.1 per cent ) than non-carers (48.8 per cent).

With regard to educational status, primary carers and non-carers are notably different, with the former being less qualified than the latter (Table 1). For example, a smaller proportion of primary carers (43.9 per cent) than non-carers (54.7 per cent) have obtained a certificate or higher qualification and a higher proportion of primary carers (40.7 per cent) than non-carers (31.6 per cent) are early school leavers.

With respect to health status also, primary carers are reportedly less healthy than non-carers. While the majority of women in both groups assess themselves as being in good health, the proportion reporting good health is much lower among primary carers (63.2 per cent) than amongst non-carers (74.0 per cent). As poorer health status is likely to impact on an individual's ability to be in gainful employment, income estimates have been calculated separately according to health status. It is important to note that health status in this study refers to self-assessed general health status rather than to a clinical assessment of health, the latter being unavailable from the data set we used. Self-assessed health status has been found to be a useful and reliable indicator in empirical research on health and has been widely used in previous research examining the association between health, employment and income (Cai & Kalb 2006; Cai 2009; Nepal et al. 2009). In the HILDA survey, data on general health status was collected through self-completion questionnaires by asking "In general, would you say your health is: Excellent, Very good, Good, Fair, Poor". In this analysis, individuals reporting their health to be 'good', 'very good' or 'excellent' were classified as being in good health, and those who assessed their health to be 'fair' or 'poor' were defined as being in poor health.

Over two-fifths (41.8 per cent) of women primary carers aged 30-64 years were caring for a husband or partner with a disability, and over one-third (34.8 per cent) were caring for their child with a disability. Some of the carers provided care for other family members such as parents.

Computation of Outcome Measures

This study estimated potential incomes over the working life (30-64 years) of women primary carers and non-carers. The main financial outcomes considered were gross incomes earned and government cash benefits likely to be received for the working life after 30 years of age. The income estimates are presented in terms of cumulative incomes over the remaining working life up to the age of 64 years. Specifically, these are synthetic estimates derived by summing up agespecific average incomes in the 2007 financial year separately for each subpopulation studied. These estimates indicate how much an average woman in a defined category would be expected to earn over her working life if she were to follow the prevailing age-specific income schedule. The income estimates are expressed in terms of 2007 dollars and no discounting has been applied. The purpose is to provide relative positions of the groups considered rather than exact dollar figures.

The method employed to derive financial outcomes follows the life-table approach established in the demographic-economic literature (e.g. Day and Newburger 2002) and thus requires age-specific annual income as its input. Age-specific gross individual income, government cash benefits and working hours for the subgroups considered in this study (Table 2) were estimated by using a generalised linear model applied to the 2007 HILDA data. The need to derive indirect estimates was due to the lack of sufficient cases available to directly calculate age-specific incomes for the combinations of factors considered here. The predictors used in the model are given in Table 2. These factors meet the need of the life-table model used in this study and so we did not use elaborate equations often used in the labour supply literature such as Bresuch and Gray (2004), which estimate the foregone earnings of motherhood.

The regression model that we used can be expressed as

Y = [[beta].sub.0] + [[beta].sub.1] *[X.sub.1] + [[beta].sub.2] *[X.sub.2] + ... + [[beta].sub.n] *[X.sub.n] + [epsilon]


Y is the variable to be estimated such as income from wages and salaries, working hours, and government cash benefits.

[[beta].sub.0] is the Y intercept;

[[beta].sub.1], [[beta].sub.2], ... [[beta].sub.n] are regression coefficients;

[X.sub.1], [X.sub.2], ... [X.sub.n] are predictors;

[epsilon] is an error term.

Only primary carers and non-carers are included in all these models. Those identifying as nonprimary carers are excluded. For partnered women, couple-level income was calculated by adding together the incomes of both partners.

The results are presented for subgroups made up by disaggregating primary carers and non-caters by partnership status (single mothers and partnered mothers), education (certificate or above, Year 12 and Years 11 or less) and general health status (good health and poor health). The results take account of the partnership, education, and health trajectories over the working life.


Labour Force Participation and Earning Potential

As would be expected, labour force participation by women primary carers was lower than that of non-carers. Primary carers were disproportionately outside the labour force. Table 3 shows that among women aged 30 to 64 years, over half (52.2 per cent) of primary carers were not in the labour force compared to just over one-quarter (28.3 per cent) of non-carers in the same age group. Further, just over one-fifth (21.4 per cent) of primary carers were in full-time employment compared to nearly two-fifths (38.6 per cent) of non-carers in the same age group. When all working as well as non-working women were considered, on average, primary carers worked 13.1 hours a week, one-third less than the hours of non-caters (21.2 hours).

Figure 1 presents the model-based estimates of average number of working hours of primary carets and non-carets by highest level of education, health and age. The findings provide further confirmation of the fact that primary carers work fewer hours per week than do non-carers across all ages. The estimated number of hours worked per week peaks around the age of 40-49 years and falls steeply thereafter for women of all categories considered in this analysis. Not surprisingly, hours worked per week are higher among women with higher levels of education and better health status. Single women tend to work longer hours than do partnered women, reflecting that many partnered women have a working partner or a spouse who brings in additional income into the family.


Financial Outcomes Over the Working Life

Reduced or foregone employment has substantial financial consequences. Overall, primary carers earn less income over their working years than non-carers with similar socio-demographic profiles. This lost income is compensated for to some extent by government cash benefits paid to carers. This is illustrated by looking at the individual and couple level incomes received over the working life from wages and salaries and government cash benefits. Income estimates are compared with those for similar women who do not have caring responsibilities. The results are summarised in Table 4.

Individual Income from Wages and Salaries

As shown in Table 4, primary carers lag far behind non-carers in terms of their prospective incomes over their working lives. The adverse impact of caring on the ability to earn a wage or salary is the greatest for early school leavers and those with poor health: this group of women carers would earn less than one-sixth of income earned by noncarers. Among those who have good health, primary carers who left school early and had no further qualifications would earn just over half of their non-carer counterparts. The gap narrows as we move up the educational level and across from poor health to good health.

Couples' Incomes from Wages and Salaries

While the earning prospect appears bleaker for partnered mother primary carers compared to sole mother primary carers at the individual level, the former group is much better off when couple-level earning is considered. A limitation of this comparison is that because relevant data were not available we did not separately estimate the couple-level incomes of women whose spouse or partner was living with a disability. Previous modelling suggests that women primary carers of a spouse with a disability are likely to be financially worse off than those whose spouses are able bodied and working (Nepal et al. 2008).

Government Cash Benefits

Table 4 shows that 30 year old single mothers, irrespective of whether they are primary carers or not, would receive a greater amount of cash benefits from the government over their working lives than their partnered counterparts. Cash transfers to sole mothers would be as high as that to couples. It is also evident that women with lower levels of education would receive a higher amount of government cash benefits if current patterns prevail into the future. Likewise, irrespective of their self-reported health status, primary carets would earn less lifetime income from wages and salaries but receive more income through government cash benefits than their non-caring counterparts (columns 3 and 6 of Table 4). This is in keeping with the intentions of government policy where eligibility for government benefits is income dependent.


This study reinforces the evidence that compared to non-carers, women primary carers who provide care to their family members with a disability have fewer opportunities to participate in the paid employment and poorer earning potential over their working life. This study also demonstrates that being partnered, having a higher level of education, and maintaining a good health would contribute positively to earning potential over the working life of women primary carers. The gap between women primary carers and non-carers is narrowed with improved educational attainment and better health status; both factors being recognised as important influence on individuals' attachment to the labour market outcomes.

This study updates previous findings from Australia that women primary caters tend to have lower lifetime incomes and are subject to a greater financial stress (Nepal et al. 2008). It also advances the previous research in two ways. First, this study refined the comparison by focusing on primary carers and non-carers and by excluding non-primary caters. Second, this study added health status as an explanatory variable, an important dimension of human capability. Good health has been found to be associated with better economic outcomes (Nepal et al. 2009). This study shows that the carer's own health status is a major contributor to their ability to contribute to the workforce and earn a living while caring.

This study builds on the findings of previous research that carers have to abandon or reduce their labour force participation to assume the caring role (Edwards et al. 2008; Gray et al. 2008). It shows that, as a consequence of caring, they are likely to experience a substantial financial disadvantage over their working life. As estimated in this study, women primary carers would earn over their working life (after the age of 30) between 10 and 74 per cent of what non-carers would be expected to earn, depending on their level of education, health and partnership status. The gap in estimated gross income over the working life is wider among those with lower education and poorer health.

The value of education has been well demonstrated in this study. Women primary carers as well as non-carers tend to have huge variations in their workforce participation and income across education levels. While women with a primary cater role tend to work shorter hours than their non-carer counterparts, those with post-school qualifications tend to work longer hours than those without these qualifications. This is manifested in lifetime incomes, with more educated carets earning more than their less educated counterparts. There are differences in educational and labour force participation rates between major categories of carers such as those receiving Carer allowance and those receiving caret Payment (Gray et al. 2008), but in this study we were not able to look at such finer details because the sample sizes were insufficient.

The importance of health status for earnings is also clearly seen in this study, as women in good health have higher incomes from wages and salaries and lower government benefits than do those with poor self-reported health (Table 4). The greatest difference in income from salaries and wages received by carers and non-carets among the study population was observed when considering the individual incomes of partnered mothers, where women primary carers in good health have the potential to earn nearly 12 times the income earned by women primary carets in poor health (column 7 of Table 4). This clearly demonstrates the significance of health status of primary carers when considering economic wellbeing, and the need to ensure that carers are supported to maintain good health.

While we have improved on previous research by adding health status as a predictor variable, we have not conducted the analysis by type of health problems. Taking on an informal caring role has been shown to impact on carer mental wellbeing, with the level of impact varying according to the nature of the relationship between the carer and the care recipient, and the stage of the caring process (Savage & Bailey 2004). Detailed analysis was constrained by the smaller sample size. Further analysis of this aspect is desirable.

Government cash benefits received by primary carets compensate, to some extent, for the income foregone from reduced ability to participate in paid employment. However, the level of compensation received through such benefits only partially offsets these losses. Not only does the foregone income makes it difficult for families of carers to meet immediate household expenses (Edwards et al. 2008), the losses extend further. With a large proportion of household expenditure going into meeting the high needs of the dependent person (Jenson & Jacobzone 2002), saving is highly unlikely. The most important is the foregone superannuation savings. As they become unable to participate in the paid employment, carers lose the opportunity to contribute to their superannuation schemes that invest towards retirement income. In an earlier study, Nepal et al. (2008) estimated that women in their early thirties, with secondary or less education, who are not in a primary carer role, would have saved approximately $100,000 of superannuation in 2006 dollar terms when they turn 65 years. But women primary carets were estimated to have saved less than $25,000 as their superannuation at the age of 65 years. It was estimated that assuming a primary carer role in the early 30s and maintaining that throughout the working life would reduce expected superannuation, on average, by about $75,000 to $80,000 in 2006 dollars. Thus, the prospect of such a small saving towards retirement leaves the caters dependent on the aged-pension system.

The study has four limitations which should be taken into account while using its findings. First, the results are based on a cross-sectional sample. Therefore, the relationships are simply associations and we cannot claim causality. The analysis was, therefore, limited to examining differences between women primary carers and non-carers with similar demographics. Second, we cannot rule out the possibility of self-selection to the caring role of individuals who may perceive they have poorer career prospects owing to reasons that may include lower education level and/or poorer health status. To the extent that this self-selection occurs in the population, the results can be seen as exaggerating the earning

gaps between carers and non-carers. Third, the dollar figures for income from wages and salaries and government benefits are synthetic estimates derived from the cross-sectional data. The estimates over the working life have been derived by assuming that the people follow the current pattern of work and earning into the future. Research suggests that for many carers the caring role may be episodic rather than life-long (Bittman et al. 2007), but such career interruptions may cause decay of human capital and negatively impact future employability and earnings (Omori 1997). The foregone earnings would be proportional to the career interruptions and it would be appropriate to refine our estimates by discounting for potential human capital decay. Here future earnings are not adjusted for inflation, wage growth and human capital decay. In sum, the estimated monetary figures can be best used as relative rather than absolute values, and are provided as a means of comparison among the groups examined. Finally, we have disaggregated the analysis by level of education, health and partnerships and we have not controlled the analysis for other factors. For example, as men tend to have higher lifetime incomes than do women (Cassells et al. 2009), inclusion of men in this analysis would have provided a gender-based comparison. However, men were excluded from the analysis because of small sample sizes resulting from fewer men taking on primary carer roles.


Earning potential of women primary carers is substantially lower relative to that of non-carers and the discrepancy extends over their working life. While carers are worse off than non-carers in terms of employment and income across the working life, background factors such as education and good health can help narrow the gaps.

Although the cross-sectional nature of the data requires caution in drawing any firm conclusion on causality, the findings of this study can certainly inform policy designed to identify carers experiencing financial burdens and to improve their wellbeing. The primary carers most vulnerable in terms of lifetime financial prospects are single mothers with poorer health and lower level of education. The findings also lend support to the current policy of providing income support to carers who forgo paid employment to fulfil their caring responsibilities. The current scheme, however, has no provision to compensate for foregone retirement savings. Therefore, it may be appropriate to include a superannuation contribution with income support provided to caters of working age. This is justifiable because in the absence of informal carers, the Government would have to arrange formal care by paying at least minimum wages and superannuation to the providers. In addition, there is a need to develop programs to provide education and training to enhance carers' employability and packages to uplift and maintain their general health. As the nature of work is changing and many people now work from home using online technologies, carets may be trained to take advantage of such emerging opportunities. In addition, part-time formal care can be arranged on a regular basis to enable carets to maintain their labour force attachment as well as their health.




For example:

* Bathing / showering

* Dressing / undressing

* Eating / feeding

* Going to toilet

* Bladder / bowel control


For example:

* Moving around away from home

* Moving around at home

* Getting in or out of a bed or chair

Communication in own language

For example:

* Understanding / being understood by strangers, friends or family, including use of sign language or lip reading


This study used unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey 2007. 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 (MIAESR). This research, funded by NATSEM, updates a study that was initially commissioned by Carers Australia, funded by Commonwealth Financial Planning, and presented at the 2008 HILDA Survey Research Conference. The findings and views reported in this paper are those of the authors and should not be attributed to FaHCSIA, the MIAESR, Carers Australia, Commonwealth Financial Planning, or NATSEM.


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Table 1: Percentage distribution of women primary carers and non-
carers aged 30-64  years by selected socio-demographic
characteristics, 2007, Australia

                                                     Ratio of
                           Primary                  percentages
                            carers     Non-carers    (Carer to
                          (per cent)   (per cent)   non-carer)
Age (years)
  30-34                          7.7         16.1      0.48
  35-39                         10.6         15.9      0.67
  40-44                          7.8         15.7      0.50
  45-49                         16.8         14.6      1.15
  50-54                         18.7         14.9      1.26
  55-59                         16.7         12.5      1.34
  60-64                         21.7         10.3      2.11
  Total                        100.0        100.0
Partnership status
  Sole mother                   14.5         13.1      1.11
  Partnered mother              44.1         48.8      0.90
  Other                         41.5         38.1      1.09
  Total                        100.0        100.0
  Certificate or above          43.9         54.7      0.80
  Year 12                       15.4         13.6      1.13
  Year 11 or below              40.7         31.6      1.29
  Total                        100.0        100.0
Self-assessed general
health status
  Poor health                   23.4         14.6      1.60
  Good health                   63.2         74.0      0.85
  Other                         13.4         11.4      1.18
  Total                        100.0        100.0

Estimated population         305,676    4,534,617

(a) Rounded to the nearest thousands.

Source: Computed from HILDA Wave 7 datafile.

Table 2: Explanatory variables used in the regression model

Variable description    Categories

Carer status            Primary carer, non-carer
5-year age groups       30-34, 35-39, 40-44, 45-49, 50-54,
                          55-59, 60-64
Partnership status      Single mother, partnered mother, Other
Highest level of        Certificate or above, Year 12, Year 11
  education               or less
General health          Poor health, good health, other
  status *

* 'Poor health' refers to poor and fair health and 'good health';
refers to good, very good and excellent health.

Other' includes no self-completed questionnaire and

Table 3: Labour force participation of women primary carers and
non-carers aged 30-64  years, 2007, Australia

                                  Primary                  (primary
                                  carers     Non-carers    carer to

Percentage not in labour force      52.2%        28.3%       1.84
Percentage employed part time       23.1%        30.9%       0.75
Percentage employed full time       21.4%        38.6%       0.55
Percentage unemployed                3.4%         2.2%       1.55
Average number of hours              13.1         21.2       0.62
  worked per week
(95% confidence interval)       (11.0-15.3)  (20.7-21.8)

Source: HILDA Wave 7 data file.

(a) Hours per week usually worked in all jobs.

Table 4: Income expected to be received from various sources over
the working life of 30 year old women -primary carers versus non-
carers, 2007, Australia

                                         Poor health

Income type,                  Primary     Non-carer     Ratio
family type and              carer ($)       ($)      carer (a)
level of education              (1)          (2)         (3)

Gross income from
wages and salaries

Individual level
income--Sole mothers

Year 11 or less                  43,500     292,500        0.15
Year 12                         236,500     511,500        0.46
Certificate or above            504,500     813,000        0.62

Individual level

Year 11 or less                  25,500     256,500        0.10
Year 12                         200,500     469,500        0.43
Certificate or above            462,500     771,000        0.60

Couple level income--
Partnered mothers

Year11 or less                  566,000   1,651,500        0.34
Year 12                       1,011,000   2,171,000        0.47
Certificate or above          1,436,000   2,635,000        0.54

Government cash Benefits
(total public transfer)

Individual level
benefits--Sole mothers

Year 11 or less                 650,000     500,000        1.30
Year 12                         614,500     465,000        1.32
Certificate or above            576,500     426,500        1.35

Individual level

Year 11 or less                 374,500     225,000        1.66
Year 12                         339,000     190,000        1.78
Certificate or above            301,000     151,500        1.99

Couple level benefits--
Partnered mother

Year 11 or less                 664,000     404,000        1.64
Year 12                         610,500     350,000        1.74
Certificate or above            541,500     281,500         192

                                      Good health             Ratio

Income type,                  Primary       Ratio     carer   Health
family type and              carer ($)       ($)       (a)     (b)
level of education              (4)          (5)       (6)     (7)

Gross income from
wages and salaries

Individual level
income--Sole mothers

Year 11 or less                 341,500     634,000    0.54     7.85
Year 12                         569,000     879,000    0.65     2.41
Certificate or above            872,000   1,180,500    0.74     1.73

Individual level

Year 11 or less                 305,000     592,000    0.52    11.96
Year 12                         527,000     837,000    0.63     2.63
Certificate or above            830,000   1,138,500    0.73     1.79

Couple level income--
Partnered mothers

Year11 or less                1,088,000   2,260,500    0.48     1.92
Year 12                       1,581,000   2,780,000    0.57     1.56
Certificate or above          2,045,000   3,244,000    0.63     1.42

Government cash Benefits
(total public transfer)

Individual level
benefits--Sole mothers

Year 11 or less                 566,000     416,000    1.36     0.87
Year 12                         530,500     381,000    1.39     0.86
Certificate or above            492,500     342,500    1.44     0.85

Individual level

Year 11 or less                 291,000     141,000    2.06     0.78
Year 12                         255,000     106,000    2.41     0.75
Certificate or above            217,500      67,500    3.22     0.72

Couple level benefits--
Partnered mother

Year 11 or less                 529,000     268,500    1.97     0.80
Year 12                         475,000     214,000    2.22     0.78
Certificate or above            406,000     146,000    2.78     0.75

(a) Ratio of lifetime incomes of primary carer to non-carer:
column (1)/column (2) = column (3), column (4)/column (5) =
column (6).

(b) Ratio of lifetime income of primary carers with good health
to primary carers with poor health: column (4)/column (1) =
column (7).

Source: Model estimates using HILDA Wave 7 data file.
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Author:Nepal, Binod; Brown, Laurie; Ranmuthugala, Geetha; Percival, Richard
Publication:Australian Journal of Social Issues
Article Type:Report
Geographic Code:8AUST
Date:Jul 9, 2011
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