Academic journal article Australian Journal of Social Issues

Are We Too Busy to Volunteer? the Relationship between Time and Volunteering Using the 1997 ABS Time Use Data

Academic journal article Australian Journal of Social Issues

Are We Too Busy to Volunteer? the Relationship between Time and Volunteering Using the 1997 ABS Time Use Data

Article excerpt


Volunteering has been defined as a freely-chosen gift of time to the community (Noble, 1991). The implicit assumption is that volunteers are those with time to spare who are willing to give their time in service to the community without receiving any monetary reward. As a result of change within the modern labour force in most developed countries and the resulting social implications, there is a shift in assumptions about which groups are the ones with time to spare. These groups could be regarded as a potential pool of volunteers.

Time is thus a key concept in understanding both why people volunteer and why they do not. The notion of "time to spare" has been attributed to why women have been the traditional source of volunteers and why policy attention is now turning towards others outside paid work, particularly the retired and the unemployed, to volunteer in response to increasing demand.

Studies of volunteering have shown that the need to fill in time is often given as a reason for volunteering (e.g. Clary, Snyder & Stukas, 1996). Similarly being too busy or having insufficient free time is often cited as a reason for not volunteering (e.g. Paolicchi, 1995). Time may place constraints on an individual's ability to volunteer. Most studies demonstrate that people are motivated by a range of factors, including time availability (Cnaan and Goldberg-Glen, 1991).

The notion of time availability is reflected in the policy response that has focused on those outside paid work as a potential source of volunteers. Organisations have assumed that this is the group with time to spare, who would thus be willing and able to undertake volunteer work. The increasing trend for older people, particularly older males, to retire from full-time work early (ABS, 1994), has led to them being targeted as a rich resource for volunteer organisations (Warburton, 1997a). The unemployed too are targeted as potential volunteers, with the current shift of focus of welfare policy from entitlement to mutual obligation (Macintyre, 1999; Newman, 1999). The government perception is that volunteering is one avenue for those outside paid work to fill their time productively.

Social and economic change is affecting the supply of volunteers. Changes in employment, work force participation, retirement, family and caring responsibilities, are all said to impact on the availability of volunteers (ACOSS, 1996). This paper discusses these issues and examines the relationship between time use and volunteering. Using the ABS 1997 Time Use data (ABS, 1997b), the paper looks for differences between those who state they have spare time and those who do not in order to identify future patterns of those who could be considered potential volunteers.

Recent social trends

In 1995, the Australian Bureau of Statistics (ABS) conducted the first national survey of volunteer work attached to its monthly labour force statistics. This survey showed that an estimated 2.6 million people or 19% of the Australian population were volunteers. The Voluntary Work Survey also showed a marked variation in volunteer participation rates according to age and life stage (ABS, 1995; ABS, 1997a).

Overall, more women than men participate in volunteer work (21.3% and 16.7% respectively) and this is consistent across all age groups (ABS, 1995). This is a consistent feature of volunteering throughout Australia's white history, where women have traditionally been the major source of volunteers, particularly in social welfare (ACOSS, 1996; Baldock, 1990).

The highest volunteer participation rates are for women and men in the 35-44 years age group (31% and 23.8% respectively). The second highest group is that of women and men aged 45-55 years (24% and 21% respectively). These two age groups are the most likely to be caring for dependent children and the high rate of volunteering reflects the involvement of parents, particularly mothers, in their children's activities. This is further evidenced by the observation that volunteer rates for the fields of education, sport and recreation, where most children's activities are located, are highest among people aged 35-44 years (ABS, 1997a). Indeed, the volunteer participation rate for married people with dependents is markedly higher than for those without dependents, dropping to 19% for women without dependents and 17% for men (1997a).

Thus, women are more likely to volunteer than men, and those in the middle years with children are the group most likely to volunteer. However, in recent years, there have been substantial social and economic changes that have the potential to impact on the capacity of these groups to volunteer. It is proposed that changes in time use are resulting in an increase in those who are simply too busy to volunteer.

Women are increasingly involved in paid work, and female participation rates have increased steadily since the late 1960s (ABS, 1998). The change in women's working lives is one of the most significant dimensions of social change in recent years (Probert, 1997). There is a range of factors that are said to contribute to the growth of female participation rates. These include labour force factors such as increased involvement in full-time education, changes in the nature of work including the growth of service industries and higher wages for women (Sheehan, 1998; ABS 1997d). They also include social factors, such as the lower birth rate, increases in child care services and changes in social attitudes towards women working (Dawkins, 1996).

The labour force participation rate for married women has increased substantially rising from 34% in 1968 to 63% in 1998 (ABS, 1998). A large proportion of this increase has occurred in the middle years, where women have returned to work during or after the care of dependent children (ABS, 1998). Overall, there has been an increasing trend for females in all age groups, except those aged between 15 and 19 years, to participate in the paid work force. Furthermore, this trend is expected to continue at least until 2016, at which time the number of women at work is projected to exceed the number of men (Sheehan, 1998).

These changes have major social implications. Among them is the impact on families, particularly as Bittman and Rice (1999) have noted the growth of unsociable working hours particularly for women. In addition, unpaid work, especially housework and child care, continues to be women's work (Bittman & Wajcman, 1999). Women are the major providers of domestic chores within the family, and with increasing longevity, by mid-life may also be caring for older relatives (Bittman, 1999). Thus it may be that women are simply too busy with balancing work and family responsibilities to have time for volunteering. Certainly, Bittman (1999) suggests that, after full time employment, responsibility for a dependent child is the factor most likely to diminish a woman's available free time.

There are little Australian data on which to assess trends relating to the patterns of male and female volunteering over time. Lyons and Fabianssion (1998) have conducted an analysis of state data, and suggest that there is an overall decline in the volunteer rate, and that the decline is actually more notable for men than it is for women. Thus there may also be factors impacting on the patterns of male volunteering.

Accompanying the increase in labour force participation for women is a steady decline in labour market participation for men, linked to the trend towards early retirement and deterioration in labour market prospects for older workers (ABS, 1994; ABS, 1999b). Over the past twenty-five years, there has been a trend towards early retirement from the paid workforce, particularly among men (ABS, 1997c; Rosenman & Warburton, 1996). Full-time participation rates for men aged 55-64 years have declined from 79% in 1973 to under 60% in 1999 (ABS, 1994; ABS, 1999a). This has been linked to an increase in real incomes and more attractive retirement income benefits as well as a deterioration in labour market prospects for older workers (ABS, 1994; Rosenman & Warburton, 1996). Early retirement is seen as one response to late life unemployment.

The growth in early retirement, particularly among men, could suggest that this is the area where volunteer growth will occur. Warburton (1997b) has noted the proliferation of programs in the United States focusing on the early retired. Some of these programs have also more recently been introduced into Australia, where the focus is on volunteering as an active form of social participation for older people (Baldock, 1999). However, current volunteer data show that there is in fact less volunteering undertaken by older people than by all other age groups, except the very youngest group, aged 15-24 years. Thus older people do not currently volunteer to the extent of those in mid life.

In addition to the trend towards early retirement particularly for men, there is also a continuing level of unemployment. Within the new social policy context of mutual obligation and active citizenship, the unemployed are being encouraged or compelled to undertake volunteer work through mechanisms such as new job search requirements and work for the dole schemes (Newman, 1999). Whether or not this is classified as volunteer work is a contentious issue. However, an implicit assumption of these schemes is that they will stimulate and encourage social responsibility to ensure a continuing supply of volunteers. Whether this will be the case has yet to be tested, although there is some evidence to suggest that it may actually be counterproductive (Stukas et al., 1999).

There is also an increasing trend towards part time work consistent with labour market trends in most developed countries (Sheehan, 1998). The number of males working part time has increased from 7% in 1988 to 12% in 1998 (ABS, 1999b). When labour market figures are disaggregated, it is apparent that part time work is most prevalent among the youngest age group, both males and females aged 15-24 years. This is reflected in both the substantial rates of underemployment amongst this age group and also the increased likelihood for workers to have more than one job (ABS, 1999c).

The growth in early retirement as well as the increase in part time work among men might suggest that they have more time to commit to volunteer work. However, there are also contradictory trends in relation to men in full time work. These trends suggest that many men in full time paid work are working longer hours.

The growth of part time work is reflected in another trend in the Australian labour market--an increased dispersion in the average number of hours worked per week. There is a decline in the proportion of people working a standard working week, with an increase in those working shorter hours (less than 20 hours per week) but also those working longer hours (over 45 hours per week) (Bittman & Rice, 1999; Sheehan, 1998). The latter trend is a recent and complex phenomenon (Dawkins, 1996). Over a ten year period from 1988, for example, full time employees working more than 49 hours per week increased from 20% to 25% (ABS, 1999b). There are also indications that the proportion of men working very long hours is almost double that of women (Verrucci, 1997).

The increase in working hours is attributed to competitive pressures in the economy, greater job insecurity, and multiple job holding (Dawkins, 1996; Verrucci, 1997). Longer working hours are concentrated in certain occupations, such as managers and professionals, tradespersons, and intermediate production and transport workers (ABS, 1999b; Verrucci, 1997). These are all occupational groups dominated by men. In addition, further analysis shows that those in the middle age categories tend to work longer hours (Sheehan, 1998). Glezer and Wolcott (1999) have also noted increased levels of stress among people trying to balance work, particularly long hours of work, and family roles.

Thus, those in mid life, particularly males, are more likely to be working longer hours. Males are more likely to be early retired. Those working part time are more likely to be women or young people, but also increasingly men. The workforce is thus becoming increasingly polarised into those working longer hours and those not working at all or working part time. Women are also carrying the additional burdens of domestic responsibilities and caring. Yet, currently, those in mid life, particularly women, are the most likely age group to volunteer, often because they have dependent children. All these factors suggest a rather complex and often contradictory picture. They also suggest that, in order to predict who is most likely to have time for volunteering, it is necessary to examine the groups that are more likely to indicate that they have time to spare.


In order to look at which groups state that they have time to spare, an analysis was conducted on the 1997 Time Use Survey data provided by the Australian Bureau of Statistics. The confidentialised unit record data were made available by an agreement between the Australian Bureau of Statistics (ABS) and the Australian Vice Chancellors' Committee Ltd. (AVCC).

The 1997 Time Use Survey examined how people allocated time to different kinds of activities in order to measure the daily activity patterns of people in Australia. The survey covered residents aged 15 years and over in private dwellings across all states and territories of Australia. The 1997 Time Use Survey consisted of detailed information on time use activities from 7,260 persons within 4,059 households.

The survey was conducted on a multi-stage area sample of private dwellings to ensure that all sections of the population are represented in the sample. In addition, each state and territory is divided into a number of areas or "strata" which consists of a number of Collection Districts. The sample was selected to ensure that each dwelling within the same stratum had an equal probability of selection. All persons within the selected dwellings were included in the survey.

Three levels of data were collected. Trained ABS interviewers collected information on the household and the adult individuals within the household, and diaries were left for all adults to detail their use of time. Completed questionnaires were obtained from 84% of all persons interviewed. The analysis reported in this paper focuses on the person level data, which includes demographic, socioeconomic and geographic indicators.


A series of multiple regression analyses was conducted using the 1997 ABS Time Use data with two separate dependent variables. The first dependent variable was "whether a person has spare time they don't know what to do with", with a high score indicating least amount of spare time. The second dependent variable was "whether a person feels rushed or pressed for time", with a high score indicating low feelings of being rushed. Predictor or independent variables included the demographic variables, age and gender; life stage variables, which were marital status and child status; and employment-related variables, relating to the number of hours a person worked, and their current main activity. All the variables included in the analysis and their relevant coding schemes are listed in Table 1. A number of the variables from the ABS dataset were categorical and were thus dummy coded for the current analyses. In each case, the reference category was decided according to theoretical considerations.

From a total sample size of 7,260 un-weighted person records, 6,843 were retained in the final analysis due to missing data. For example, persons who did not state the number of hours they worked or whether they had spare time were not included in the analysis.

In order to examine the predictive ability of the demographic, life stage and employment related variables, a multiple regression analysis was performed, with spare time as the dependent variable. All the independent variables were included in the analysis. First, the bivariate correlations were examined to ensure that multicollinearity was not a threat to the stability of the regression analysis (Bryman & Cramer, 1990). (2)

The summary statistics, including the means and standard deviations of all the variables, are listed in Table 2, with the results of the first multiple regression analysis. To examine which variables had a distinctive effect on the dependent variable, the beta coefficients were tested for significance. Because of the number of predictor variables in the research and the large sample size, only findings significant at p < .01 were interpreted. This procedure was adopted to reduce the number of Type 1 errors.

Results of the analysis showed that there was a significant multivariate effect when all the predictor variables were in the model. (3) Overall, the set of variables accounted for 10% of the variance in the dependent variable, spare time (R2 = 0.10). While this is not a large proportion of variance, as noted in Table 2, there were some important significant effects between the independent variable and some of the predictor variables in the model. In relation to age, the reference group (40-44 years) were significantly less likely than the three youngest age groups and more likely than the five oldest age groups to state that they have time to spare. Thus, contrary to expectations, all of the over 55 year age groups, many of whom were retired, were less likely to state that they have time to spare. There were also significant differences by gender with females indicating less spare time than males.

In relation to the two life stage variables--marital status and child status--only marital status showed significant differences, with those married less likely to have time to spare. The two employment related variables also showed some significant differences. In relation to the hours worked, those working a standard working week (the reference category) were significantly less likely to have spare time than those not working at all, but more likely to have spare time than those working more than 49 hours a week. In relation to current main activity, those working (reference category) were significantly less likely to have time to spare than those looking for work or those caring for others or themselves.

Further analyses were conducted to determine if the relationship between the variables was due to the interaction or mediation of other independent variables in the model. For example, tests were conducted to see if having a child mediated the relationship between marital status and spare time. Results indicated that there were no significant interactive effects in the data. (4)

In order to explore the data further, a second series of multiple regression analyses was performed with the same predictor variables, but with the second dependent variable, feeling rushed or pressed for time. In this analysis, 6,900 person records were retained and the correlation between the two dependent variables, spare time and feels rushed, was -.18. As can be seen in Table 3, the second analysis revealed a similar pattern of results to the first, although there was a stronger predictive relationship? Overall the model accounted for 26% of the variance in whether respondents felt rushed (R2 = 0.26).

Individual differences revealed that the middle age group (the reference category, 40-44 years) was significantly more likely to feel rushed than either the youngest age group or the five oldest age groups. Females were significantly more likely to report feeling rushed than males; those who were married and those who had children were also significantly more likely to report feeling rushed.

There were also some differences in the employment related variables. Those working a standard working week (the reference category) were significantly more likely to feel rushed than those not working at all, and significantly less likely to feel rushed than the two groups working longer than 41 hours per week. In relation to the current main activity, those working (reference category) were less likely to feel rushed than those studying and more likely to feel rushed than those caring for themselves or others.


These analyses provide some indication of the groups who have time to spare, and who might be considered a potential source of volunteers. As mentioned above, there were some significant predictors in both analyses. Those who were married or living with a partner were more likely to feel rushed and less likely to have time to spare. The evidence is that marriage shortens the free time available for both partners (Bittman, 1999). This is the group most likely to have dependent children, and indeed, those who were married and who had children were more likely to report feeling rushed. Females too are more likely to report less spare time and a greater likelihood of feeling rushed. As discussed, women today are more likely to be in paid work, and yet still maintain primary responsibility for household chores (ABS, 1998; Bittman & Wajcman, 1999).

These findings suggest important implications for the future of volunteering. Those with dependent children are more likely to volunteer than those without, and women currently volunteer more than men (ABS, 1995). However, these groups are those reporting little time to spare and may not be prepared to volunteer in the future, particularly if volunteering is viewed as a disposable activity in their lives.

These data also demonstrate a strong relationship between time availability and working. Generally those looking for work reported more spare time and feeling less rushed than those in paid work. That those who were studying reported less spare time than those working may be a result of the growing need to work as well as study in order to afford educational costs. There is an increasing polarisation in the workforce between those in work and those outside work, with an increase in working hours for those in full time work with a quarter of all full time workers working more than 49 hours per week (ABS, 1999b). Those who are working longer hours are least likely to say they have time to spare and most likely to say that they feel rushed. While this is hardly a surprising result, there are important social implications from this finding.

Certainly the trend towards longer working hours is going to impact on time availability for other activities. Those who currently volunteer may simply become too busy. This is important given the association of longer working hours with middle age and thus dependent children, the group that are currently most likely to volunteer. Men in full time work are more likely to be working longer hours, which may account in part for the overall decline in the male volunteer rate (Lyons & Fabiansson, 1998).

The findings for age are quite complex. According to these data, the younger age groups report having more time to spare and the youngest age group (15-19 year olds) report feeling less rushed or pressed for time. These age groups are those who are most likely to be single, without children and unemployed, and hence less likely to have these major time commitments. The older age groups, particularly those aged over 55 years, also report feeling less rushed than those in mid-life. This reflects their life stage as many will no longer have dependent children, and many will be retired. However, perhaps more surprisingly, the older age groups report having less spare time than those in mid-life. This is an interesting and consistent result across these older age groups. A suggestion can be posited for this finding. Despite assumptions that retired people have time to spare, there is growing evidence that this is not necessarily the case (Warburton, 1997b). Many older people are busy across a range of activities and interests, including caring roles and providing formal and informal support to their families and communities. Thus, they simply may not have time to spare.

There are implications of these results for the recruitment of volunteers for nonprofit organisations. As discussed, policy attention in recent years has turned to those outside paid work as a major source of volunteers. These data would suggest that those not working, particularly the young, are the main groups who state they have time to spare. Hence, according to this rationale, these groups may be willing to volunteer.

There are some important reservations to note in this analysis. Use of Australian Bureau of Statistics data allows researchers the opportunity to undertake analyses of large, representative data sets, however, there are also some limitations using these data. First, care should be taken in interpretation, because of statistical power issues affected by the large sample size. The overall low level of variance explained by the model suggests that it is hard to predict who has time to spare and who states that they feel rushed. Further, there may be other limitations in performing secondary data analyses (Maher & Burke, 1991). In the present analyses, there may be considerable personality variations in response to questions such as availability of spare time or sense of feeling rushed. For example, some may feel it is not socially acceptable to admit they have spare time they do not know what to do with. In similar circumstances, some people appear rushed when others may say they have little to do.

Similarly, it is important to be careful in interpreting other statistical data. For example, national volunteer data show that those in mid life and particularly those with dependent children are more likely to volunteer than other age groups (ABS,1995). However, a disaggregation of these figures shows that these groups actually donate less hours proportionately to their volunteering than older age groups. The quantity of time donated is also important in relation to understanding volunteering, and here it should be noted that older people, although less likely to volunteer, are more likely to give more time to their volunteer activities (ABS,1995). This may be a partial explanation as to why older groups report less time to spare.

More importantly, there may be assumptions made about the relationship between having spare time and volunteering. Some may report spare time and wish to use that time to volunteer, but may be unable to do so because of other barriers such as cost, opportunities, transport and so on (Caro & Bass, 1995). More fundamentally, those with spare time may simply not wish to volunteer. It is interesting to note that a longitudinal analysis of time use data reveals that, contrary to anecdotal reports, people generally have far more time to spare than previous generations (ABS, 1997b; Bittman, 1999; Gershuny, 1992). Whether they choose to spend their time in volunteering is another issue. This is an interesting dimension because time availability is the foundation of the organisational as well as governmental policy response to increasing demand for volunteers.

People may not volunteer simply because they have spare time. They may choose to do a range of other things with their time. As discussed, volunteer motivation is complex and multifaceted. Having spare time to volunteer may be one factor in the gamut of motivations. People volunteer for a range of reasons, which can be classified as egoistic reasons and altruistic reasons (Piliavin & Charng, 1990; Simmons, 1991). In most motivational studies, helping others emerges as a uniformly strong factor (see, for example, Baldock, 1990; Clary, Snyder & Stukas, 1996; Warburton & Terry, 2000). While this may actually be a truism as helping others is the essence of volunteering, nevertheless, it is interesting that respondents express an other-centred motivation (Story, 1992). Altruism has been linked to the development of a strong internalised moral system. Clearly not all of us feel morally obliged to volunteer, and may prefer to spend our time in other pursuits. Indeed, it may be that an activity motivated at least in part by altruism does not sit very comfortably with competition and a market economy. Moves towards privatisation and user pays may actually run counter to notions of freely given time. Perhaps Australians may be less willing to volunteer in the present economic and political context, regardless of time availability (Pusey, 2000).

This all suggests that focusing on time availability, whether it is lack of spare time as a barrier to volunteer work or volunteering in order to fill time, neglects other important aspects of the volunteer decision. It particularly neglects the potential importance of external influences, such as social norms (Warburton & Terry, 2000). Whether we volunteer or not may also be a result of a range of socio-demographic factors such as gender or social class (Baldock, 1990; Warburton et al., 1998).

The question of why people volunteer is thus complex. Having time to spare is one component, but there are a range of motivations and factors that influence the decision to volunteer. This paper has incorporated an analysis of the groups presumed to have time to spare. However, further research needs to be undertaken to assess to what extent spare time impacts on whether an individual performs volunteer work.

Table 1.

Variables included in the standard multiple regression
analysis and their coding scheme.

Variable                 Coding scheme

Dependent variables
Spare time               1 Always have spare time doesn't know what to
                           do with
                         2 Often have spare time doesn't know what to
                           do with
                         3 Sometimes have spare time doesn't know what
                           to do with
                         4 Rarely have spare time doesn't know what
                           to do with
                         5 Never have spare time doesn't know what to
                           do with

Feels rushed             1 Always feels rushed or pressed for time
                         2 Often feels rushed or pressed for time
                         3 Sometimes feels rushed or pressed for time
                         4 Rarely feels rushed or pressed for time
                         5 Never feels rushed or pressed for time

Predictor variables

Age                      A1   15-19 years
(Dummy coded)            A2   20-24 years
                         A3   25-29 years
                         A4   30-34 years
                         A5   35-39 years
                         Reference category 40-44 years
                         A6   45-49 years
                         A7   50-54 years
                         A8   55-59 years
                         A9   60-64 years
                         A10   65-69 years
                         A11   70-74 years
                         A12   75 years and over

Sex                      0 Male
                         1 Female

Marital status           0 Currently single (separated, divorced,
                           widowed, never married)
                         1 Married or de facto relationship

Child status             0 Without dependent children, whether married
                           or single
                         1 With dependent children, whether married
                           or single

Number of hours          W1   0 hours
worked                   W2   1-15 hours
(Dummy coded)            W3   16-24 hours
                         W4   25-34 hours
                         Reference category 35-39 hours
                         W5   40 hours
                         W6   41-48 hours
                         W7   49 hours and over

Current main activity    Reference category--Working
(Dummy coded)            M1--Looking for work
                         M2--Working in unpaid voluntary job
                         M3--Home duties
                         M4--Child care
                         M7--Voluntarily inactive, Own illness/injury,
                         Own Disability/limitation in personal
                         care/Looking after ill Person/person with
                         a disability/aged person
Table 2.

Summary statistics and results of the standard multiple regression
analysis for the prediction of spare time.

Variable                M (sd)(a)       Beta     Partial correlation

Dependent: Spare time   3.89 (0.88)

Predictor variables

   A1                   0.09 (0.30)   -0.10 ***                -0.07
   A2                   0.09 (0.28)   -0.08 ***                -0.06
   A3                   0.10 (0.30)   -0.06 ***                -0.05
   A4                   0.10 (0.30)   -0.02                    -0.01
   A5                   0.11 (0.34)    0.00                     0.00
   A6                   0.10 (0.30)    0.02                     0.02
   A7                   0.08 (0.27)    0.03                     0.02
   A8                   0.06 (0.24)    0.04 **                  0.03
   A9                   0.06 (0.24)    0.07 ***                 0.06
   A10                  0.05 (0.21)    0.06 ***                 0.05
   A11                  0.04 (0.20)    0.07 ***                 0.05
   A12                  0.05 (0.21)    0.06 ***                 0.05

Sex                     0.52 (0.50)    0.14 ***                 0.12
Marital status          0.64 (0.48)    0.09 ***                 0.07
Child status            0.41 (0.49)    0.04                     0.03

Number of hours worked
   W1                   0.39 (0.49)   -0.12 ***                -0.05
   W2                   0.08 (0.27)   -0.02                    -0.01
   W3                   0.06 (0.23)   -0.01                    -0.01
   W4                   0.04 (0.21)   -0.00                     0.00
   W5                   0.11 (0.32)    0.01                     0.01
   W6                   0.08 (0.26)    0.03                     0.02
   W7                   0.15 (0.35)    0.10 ***                 0.07

Current main activity
   M1                   0.03 (0.18)   -0.05 ***                -0.04
   M2                   0.01 (0.11)    0.02                     0.02
   M3                   0.22 (0.41)   -0.03                    -0.01
   M4                   0.02 (0.15)    0.02                     0.02
   M5                   0.09 (0.28)    0.03                     0.02
   M6                   0.09 (0.28)   -0.03                    -0.02
   M7                   0.04 (0.19)   -0.08 ***                -0.06

R = 0.32
R_= 0.10
Adjusted R_ = 0.10
F(29, 6813) = 26.53, p < .0001

(a)  Standard deviations appear in brackets.

** p < .01, *** p < .001
Table 3.

Summary statistics and results of the standard multiple regression
analysis for the prediction of feels rushed.

Variable                  M (sd)(a)      Beta

Dependent: Feels rushed   2.83 (1.05)

Predictor variables
   A1                     0.09 (0.30)     0.05 ***
   A2                     0.09 (0.28)     0.02
   A3                     0.10 (0.30)    -0.02
   A4                     0.10 (0.30)    -0.03
   A5                     0.11 (0.34)    -0.03
   A6                     0.10 (0.30)     0.01
   A7                     0,08 (0.27)     0.01
   A8                     0.06 (0.24)     0.04 **
   A9                     0.06 (0.24)     0.08 ***
   A10                    0.05 (0.21)     0.10 ***
   A11                    0.04 (0.20)     0.10 ***
   A12                    0.05 (0.21)     0.13 ***

   Sex                    0.52 (0.50)    -0.15 ***
Marital status            0.64 (0.48)    -0.07 ***
Child status              0.41 (0.49)    -0.15 ***
Number of hours worked
   W1                     0.39 (0.49)     0.16 ***
   W2                     0.08 (0.27)     0.05
   W3                     0.06 (0.23)     0.01
   W4                     0.04 (0.21)     0.01
   W5                     0.11 (0.32)    -0.01
   W6                     0.08 (0.26)    -0.04 **
   W7                     0.15 (0.35)    -0.13 ***

Current main activity
   M1                     0.03 (0.18)     0.01
   M2                     0.01 (0.11)    -0.02
   M3                     0.22 (0.41)     0.03
   M4                     0.02 (0.15)    -0.02
   M5                     0.09 (0.28)    -0.05 **
   M6                     0.09 (0.28)     0.04
   M7                     0.04 (0.19)     0.05 ***

R = 0.51
R_= 0.26
Adjusted R_ = 0.26
F(29, 6801) = 81.64, p < .0001

Variable                       Partial correlation

Dependent: Feels rushed

Predictor variables
   A1                                         0.04
   A2                                         0.01
   A3                                        -0.02
   A4                                        -0.03
   A5                                        -0.03
   A6                                         0.01
   A7                                         0.01
   A8                                         0.04
   A9                                         0.07
   A10                                        0.09
   A11                                        0.08
   A12                                        0.11

   Sex                                       -0.15
Marital status                               -0.06
Child status                                 -0.13
Number of hours worked
   W1                                         0.07
   W2                                         0.03
   W3                                         0.01
   W4                                         0.01
   W5                                        -0.01
   W6                                        -0.04
   W7                                        -0.11

Current main activity
   M1                                         0.01
   M2                                        -0.02
   M3                                         0.02
   M4                                        -0.02
   M5                                        -0.03
   M6                                         0.03
   M7                                         0.05

R = 0.51
R_= 0.26
Adjusted R_ = 0.26
F(29, 6801) = 81.64, p < .0001

(a) Standard deviations appear in brackets.

** p < .01, *** p <.00


The authors would like to thank the Australian Bureau of Statistics for allowing University researchers to access Confidentialised Unit Record Files through the agreement between the ABS and the Australian Vice Chancellors' Committee.

Thanks are also due to Mr Barry Maher of The University of Queensland for help with data management; and to Dr Mark Bahr of The Faculty of Social and Behavioural Sciences, The University of Queensland for statistical advice. In particular, we would like to acknowledge the generous advice and encouragement provided by Associate Professor Michael Bittman of the Social Policy Research Centre, University of New South Wales.


(1.) An example of how variables were dummy coded according to theoretical considerations is the age variable. When creating the dummy variable, the reference category, aged 40-44 years, was chosen because the literature suggested that this is the age group most likely to have children, be working longer hours, and also to volunteer. This allows for a consideration of time availability in relation to the other age groups.

(2.) Preliminary analyses included an additional employment related variable, labour force status. This was a dichotomous variable indicating whether or not a person was employed. Due to the high correlation between labour force status and number of hours worked (r > .90), this variable was omitted from the final analysis. Furthermore, number of hours worked was found to mediate the relationship between labour force status and spare time. In subsequent multiple regression analyses, it was found that number of hours worked satisfactorily accounted for the variance attributable to labour force status and that this variable could be removed without loss of predictive ability (the overall reduction in R the multiple correlation coefficient, when labour force status was omitted was only .001). None of the remaining correlation coefficients exceeded .60.

(3.) Results from the first regression analysis were as follows, R = .32, F(29, 6813): 26.53, p < .0001.

(4.) Moderator analyses were performed to determine if any interactive effects were operative. Predictor variables were centred around their respective mean and interactive terms were created according to Aiken and West (1991). Although a number of moderator effects were found to be statistically significant, primarily due to the statistical power afforded by the large sample size, no effect was found to predict more than 1% of the variance and hence, no moderator effects were deemed to be important predictors of spare time. Mediational analyses were also performed to determine if child status mediated the relationship between marital status and spare time, however, the results indicated that this relationship did not exist.

(5.) The second series of analysis revealed that, with all the predictors in the equation, R = .51, F(29, 6801): 81.64, p < .0001.


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Jeni Warburton is a Lecturer in the School of Social Work and Social Policy, University of Queensland. Tim Crosier is a doctoral student in the School of Psychology, University of Queensland.

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