Deferring a university offer in rural Australia.
Polesel, John
Introduction
This article examines the transition of rural school-completers in
the Australian state of Victoria who, when first contacted as part of a
tracking study five months after leaving school, had deferred an offer
of a place in university. 'Deferment (sometimes known as deferral)
is the process whereby successful applicants to a particular course are
able to delay commencement of their studies in that course'
(University of Melbourne, 2007). The issue of theoretical and practical
interest that this article examines is whether this phenomenon of
deferral constitutes a disadvantage for young people living in rural
areas. For example, are these deferrers 'lost' to the system?
Do they eventually take up their places? Are some groups less likely to
take them up than others? What happens to the rest? Of particular
interest is the question regarding what barriers might prevent some
groups from taking up their place. Past studies indicate that
cost-related factors and financial barriers are prominent amongst the
reasons given by young people for deferring, particularly among regional
deferrers (Teese, Clarke & Polesel, 2007, p. 57). Do these barriers
continue to play a role? And finally, what is the experience of those
who enter university? Do they continue and thrive in their studies?
The argument that rural and remote communities experience
relatively more severe economic and social hardship than their
metropolitan counterparts--a phenomenon described as 'regional
disadvantage'--has been widely applied in the fields of
unemployment, labour distribution and business investment (for example,
Kilmartin, 1993; Western Research Institute, 2004). In the Australian
context, it has also been applied to educational issues relating to the
challenges of curriculum provision in schools and the need to maximise
access to technical and trade training facilities in the VET sector
(Parliament of Victoria, 2006). In the domain of higher education,
research has noted the need to support students from rural and remote
settings who move away from home to attend university (Australian Vice
Chancellors' Committee, 2007) and the higher costs of university
study for rural students (Parliament of Victoria, 2006). The combined
impact on university participation of low socio-economic status and
rurality was also highlighted in a recent review of higher education in
Australia (Department of Education, Employment and Workplace Relations,
2008). The research has further noted the higher per-student costs of
delivering university courses in non-metropolitan settings (LaTrobe
University, 2006; University of Ballarat, 2007). Consequently, state
governments have called on the Commonwealth to recognise these higher
costs and to allocate greater numbers of university places to regional
campuses of universities (Parliament of Victoria, 2006). Higher
education participation rates in rural communities have long been known
to be lower than those in metropolitan areas (Stevenson, Maclachlan
& Karmel, 1999; Marks et al., 2000) and Australian federal
government data suggests an increasing gap over time between the
proportions of metropolitan and non-metropolitan people with tertiary
qualifications, with the lowest proportions to be found in the most
remote areas (Australian Bureau of Statistics, 2008).
But the role of deferral within the broader context of relatively
low university participation in rural Australia has received little
attention. This is partly due to the comparative recency of any
published data that might support an analysis of the phenomenon on a
regional basis. Tracking studies in Queensland and Victoria have only
recently allowed the calculation of reliable estimates of rates of
deferral for metropolitan and non-metropolitan school-completers. In
other states, tracking studies are largely absent or relate to sample
studies, such as recent New South Wales studies comparing samples of
school completers (for example, Helme et al., 2007) or to
sector-specific cohorts, such as the Western Australian tracking program
that focuses on state school students only.
In Victoria, an analysis of Department of Education and Early
Childhood Development (DEECD) tracking data (author's analysis)
confirms the greater propensity for school completers from rural and
provincial regions of Victoria to defer a university place, compared
with school completers from Melbourne. Moreover, the author has found
that the rate of deferral has risen steadily since tracking of school
completers first began in Victoria in 2004, and that the rate of
deferral amongst regional young people has grown even more rapidly than
that of their Melbourne metropolitan counterparts, widening the gap
between the two groups (see Table 1). In regional Victoria this rate has
risen from 9.9 per cent in 2004 to 15.9 per cent in 2007, although in
terms of actual numbers, the rise is even more significant--from 541
young people in 2004 to 1403 young people in 2007 (author's
analysis of DEECD data). Recent tracking work carried out in Queensland
(for example, Department of Education, Training and the Arts, 2007) also
confirms the tendency of non-metropolitan school-completers to defer
university places at a higher rate and suggests that the phenomenon of
higher rates of deferral amongst non-metropolitan school completers may
be a widespread occurrence across rural Australia.
Methodology
This study was commissioned by a selection of 14 non-metropolitan
local learning and employment networks (LLENs) in Victoria and
coordinated by the Youth Affairs Council of Victoria .
The aim of the study was to identify Year 12 completers from 2006
in non-metropolitan Victoria who had deferred a place at university and
survey this group in 2008 and 2009 to determine their post-schooling
destinations and pathways.
The survey was designed to capture the transition experiences over
a two-year period of rural school-completers who had deferred a place at
university. In broad terms, the target sample was school completers from
the 2006 Year 12 cohort who were located in non-metropolitan Victoria
and who had deferred a university offer in 2007. For the purposes of
this survey, the sample was defined as consisting of Year 12 school
completers, who met these criteria:
* identified as deferrers when contacted during the 2007 DEECD On
Track survey
* attended a school located in one of the 14 rural LLENs
participating in the study and
* agreed to be contacted again as part of the longitudinal deferral
study.
Table 2 presents the designed and achieved sample sizes, broken out
by participating LLEN. The 'deferrals' column reports the
number of school completers who identified as deferrers when first
contacted in 2007. The next column reports the number who were recruited
(who agreed to be contacted again as part of the deferral study in
2008). The 'surveyed' column reports the numbers of actual
participants in the survey, while the final column reports participation
in the survey as a proportion of all identified deferrers, as shown in
the 'deferrals' column.
Both the recruitment and participation rates for the study were
very high. Of the 930 deferrers identified in 2007, 96.5 per cent agreed
to be recontacted (897 recruits). Of this group, 89.9 per cent were
contacted and participated in the study in 2008 (806 respondents).
Overall, 86.7 per cent of the eligible cohort took place in the survey,
with rates of participation varying from 80.4 per cent to 94.7 per cent
across individual LLENs.
While these survey participation rates point to a robust and
reliable sample for the purposes of this analysis, they should not be
taken as an accurate indicator of the dimensions of the phenomenon of
early leaving. The numbers above almost certainly underestimate the
numbers of deferrers in each LLEN. DEECD tracking studies typically
survey approximately only 70 per cent of the eligible school-completer
cohort (e.g. Teese, Clarke & Polesel, 2007), suggesting that there
are considerably more deferrers in the regional communities than the 930
participating in the study.
It is also important to examine the achieved sample in terms of its
achievement, gender, and socio-economic status profiles. Table 3
compares these characteristics of the survey cohort with those of all
deferrers identified in the 2007 tracking survey. In terms of gender,
the two groups are identical. The over-representation of female
respondents reflects the higher propensity for girls to enter
university. In terms of achievement, which is based on a composite
measure of General Achievement Test (GAT) scores, the two groups are
also very similar, suggesting that the non-metropolitan deferrers in our
survey sample have a very similar achievement profile to the broader
population of deferrers across Victoria. But the final factor,
socio-economic status--which is based on a socio-economic index for
areas (SEIFA) value based on their home address--shows significant
differences between the survey group and the broader population of
deferrers, as identified in the 2007 survey. While deferrers in the
broader population are more evenly dispersed across the four categories
of socio-economic status, those in the sample are heavily concentrated
in the two lowest categories of socio-economic status, with nearly half
(44.8 per cent) of the group in the lowest quartile of socioeconomic
status.
These findings suggest that the higher deferral rates evident
amongst non-metropolitan students may be influenced by the impact of
socio-economic status on the decisions taken by this group of school
completers, particularly as this relates to the costs of living away
from home, course fees and costs of travel--issues which are considered
in detail below.
Do deferrers take up their offers?
Perhaps the most important question to be answered in this study is
whether young 'deferrers' eventually take up their offer of a
place at university. A preliminary response may be gained by examining
the destinations of these young people in their second year out of
school. Young people contacted in the survey at this point were asked
detailed questions regarding both their study and their labour market
situations. Their 'main' destinations, broken out by gender,
are reported in Table 4. This table shows that 69.9 per cent of the
group were attending university in 2008. A further 9.3 per cent were in
a VET program, most (7.2 per cent) at a level of Certificate IV or
higher. A further 3.1 per cent were combining employment with training
in the form of an apprenticeship (1.2 per cent) or a traineeship (1.9
per cent). In total, 82.3 per cent were in some form of recognised
education or training. The remaining respondents were not in education
or training of any kind. Most (16.2 per cent of the cohort as a whole)
were in paid work--11.4 per cent in fulltime positions and 4.8 per cent
in part-time positions. Few were unemployed (1.0 per cent), and a very
small group (0.5 per cent) was inactive, which means in this context
they were not in education or training and were neither in paid
employment nor looking for paid employment.
Table 5 presents a cross-tabulation of study level and labour
market destinations, providing a more nuanced picture than the main
destinations presented in Table 4. For example, while university degree
students were previously presented as a single category, it is possible
to see now their labour market destinations--paid work either full-time
or part-time, unemployed or not in the labour market. This is also the
case for young people in other study destinations. This shows that the
proportion of young people in the labour market is actually much higher
than shown in Table 4. For example, the number of part-time workers and
the number of young people seeking paid work is much higher than can be
gleaned from the summary destinations, even though most of these are
university or VET students whose labour market status may not constitute
their primary activity or focus. This is important, since the labour
market status of students (their need to work part time or the fact that
they are actively seeking paid work) tells us much about their financial
status and financial needs--a theme which is explored in the ensuing
discussion.
Table 4 also shows the main destinations of deferrers, broken out
by gender. In this case, it would seem that gender does not play a major
role in determining the outcomes of this group of school leavers. Male
and female deferrers are very similar in their outcomes two years out
from school. The main differences are that females not in education or
training are more likely to be working part time, while their male
counterparts are more likely to be working full time. Young men are
slightly more likely to be in university degree-level programs, while
young women are slightly more likely to be in VET programs.
Socio-economic status and achievement differences
Socio-economic status plays a pronounced role in determining the
outcomes of this group of school leavers (see Table 6). It has already
been noted that the survey sample as a whole is made up predominantly of
respondents with a background of low socio-economic status, with 82.0
per cent of the cohort belonging to the two categories with the lowest
socio-economic status, reflecting the relatively higher exposure of
these young people to the factors associated with socio-economic
disadvantage. But respondents who made the transition to university in
2008 were much more likely to come from the two higher quartiles of
socio-economic status, suggesting that the financial implications of
university study continue to have an impact on the pathways of regional
deferrers two years out of school. Chi-square analysis confirms the
statistical significance of the relationship between university entry
and socio-economic status, underlining the social pattern evident in
university transition.
Achievement may also be seen to play a role in determining the
outcomes of this group of school leavers (see Table 7). Respondents who
made the transition to university in 2008 were much more likely to come
from the two higher quartiles of achievement, compared with those who do
not take up a place at university. Respondents in the highest quartile
of achievement were more than twice as likely to enter university as
those in the lowest quartile of achievement. Chi-square analysis (see
Table 7) denotes the statistical significance of the relationship
between university entry and achievement.
Chi-square analysis of the influence of socio-economic status,
controlling for achievement, fails to provide strong evidence of the
effect of socio-economic status, independent of achievement, on
outcomes. This may be due to the large number of cells with small counts
and the relatively small size of the achieved sample. But a previous
study (Teese et al., 2006) has noted a similar relationship between
socio-economic status and outcomes to that described in the current
research--a relationship that suggests that deferrers with a background
of high socio-economic status are more likely than their peers with
lower socio-economic status to take up their university offer one year
after deferring. James (2002) has also noted the negative and cumulative
effects of low socio-economic status and rurality, at least with respect
to young people's aspirations of attending university.
Those who have taken up education or training
When we examine the situation of those who have taken up a place in
education or training, the study shows that the most likely outcome for
a regional deferrer two years out from school is the commencement of the
university course they deferred or of another university course. This is
perhaps unsurprising, given that all of the young people in this study
had successfully applied for a place at university. In all, 563 of our
806 deferrers took up a place at university. Of these, most (458) took
up the course they had deferred the previous year. A further 105 took up
a different university course. A further 12.4 per cent of respondents
entered vocational education and training, including apprenticeships and
traineeships. Most of these (9.3 per cent) were in campus-based study in
Technical and Further Education (TAFE) institutes and private VET
providers. Of these, 7.2 per cent were in courses at Certificate IV
level, diploma level or advanced diploma level. A further 2.1 per cent
were in courses at Certificate I, II or III level. In addition, a small
proportion of the respondents entered traineeships (1.9 per cent) and
apprenticeships (1.2 per cent).
Given that this article is concerned with the potential impact of
regional disadvantage on the study and training choices of young people,
it is appropriate at this point to ask whether there are discernible
patterns in the location of the study and training destinations of the
young people in this study. Table 8 below reports the proportions of
respondents in the survey attending Melbourne metropolitan, rural and
interstate locations broken out by university and VET providers. This
table shows that the respondents were more likely to attend metropolitan
than non-metropolitan providers, both in VET institutions and
universities. Amongst those students attending VET institutions, 54.9
per cent were in a Melbourne metropolitan location. A further 2.2 per
cent were in interstate VET institutions, including TAFEs. The remaining
students (42.9 per cent) were in non-metropolitan VET providers.
Amongst those students attending universities, the proportion in
the capital city institutions is even greater, with 58.6 per cent in
Melbourne universities. A further 6.4 per cent were attending interstate
universities. The remaining university students (35.0 per cent) were in
rural and provincial Victorian universities. This is not unexpected,
given the higher proportion of tertiary places offered in metropolitan
settings in every Australian state. It does illustrate that, for the
majority of the survey cohort, entering tertiary education means going
to the city, with the attendant issues of moving away from home or
travelling significant distances.
When the students were asked to estimate the distance of their
place of study from their family home, it became clear that the vast
majority had taken up study at considerable distances from home (see
Figure 1). More than six in ten (61.9 per cent) of the respondents were
studying at a location 150 kilometres or further from their family home.
More than three-quarters (76.1 per cent) were at a location 100
kilometres or further from home. In total, 85.3 per cent were studying
in a location at least 50 kilometres away from their home.
Another issue of relevance to the transition of regional young
people is their work status while at university or TAFE. More than half
of all the campus-based students--excluding the 25 apprentices and
trainees--in our study (331 respondents or 51.9 per cent) reported that
they were working while studying. Given the importance of financial
considerations in the take-up and successful continuation of studies, it
is useful to consider the number of hours worked by these students (see
Table 9). It may be seen that the majority were working 10 or fewer
hours per week. A large group worked between 10 and 20 hours per week,
and a smaller but significant group was working 21 hours or more per
week. The longer hours worked by nearly half of the students raise some
legitimate concerns regarding the balance of study and work that these
students are able to achieve. It is important that this data is
reconsidered when the cohort is recontacted in 2009, in order to assess
whether students' workloads have had an impact on rates of
continuation and completion in study.
Finally, it should be noted that many students (13.9 per cent of
university students and 14.7 per cent of the campus-based VET
students--89 in all, again with the 25 apprentices and trainees
excluded) reported that they were seeking paid work. When added to the
331 respondents who were working while studying, we can see that work is
of considerable importance to the majority of those respondents who
actually make a successful transition to tertiary education.
Those who have not taken up education or training
The proportion of respondents who were not in education or training
when contacted in 2008 was relatively small--approximately 18 per cent
(or 143) of our cohort. The survey shows that most of these were
employed (11.4 per cent of the total cohort working full time and 4.8
per cent working part time), with 1.0 per cent of the total cohort
unemployed and 0.5 per cent not in the labour market. The reasons given
by these respondents for not taking up their offer of a university place
or any other study or training option reveal much about the barriers
faced by young people in rural Victoria. The young people were given a
range of options to choose from to describe their reasons for not being
in education or training. They were allowed to choose multiple options,
as the motivations for not studying may be complex and cumulative in
effect. Figure 2 illustrates the reasons given in percentage terms.
The most common reason for not taking up study or training, given
by nearly six in ten of the group, was that they had found something
else. Nearly half were planning to travel or taking a gap year, and over
four in ten were not yet ready for any more study. The interplay of
these reasons suggests a complex and varied mix of motives for not being
in study or training. But the financial and distance-related barriers so
evident in the deferrers' thinking when first contacted in 2007
have not disappeared (Teese, Clarke & Polesel, 2007). Although it
might be argued that over four in five of the original group of
deferrers have taken up a place in tertiary education and have thus
overcome these challenges, cost-related barriers remain significant
among the reasons given by those who are still not in education or
training.
Approximately four in ten report that they could not support
themselves and that the costs of study are a barrier. Financial pressure
on their family, concern regarding HECS debts and the costs of travel
were all nominated by about one-quarter of the respondents, and also
reflect the continuing importance of financial barriers to the
participation of non-metropolitan youth in education and training. The
need to qualify for Youth Allowance and only being able to get into a
fee-paying course were also among the financial reasons cited. In all,
approximately two-thirds (66.4 per cent) of those not in education or
training nominated at least one of these financial barriers as a reason
for not being in education or training in 2008. Other related barriers
that were nominated included the perception that study would require
them to leave home or that their preferred course was not offered
locally--each accounting for over three in ten respondents.
Of this broader group of 143 respondents not in education or
training, most (131) were in paid work. Figure 3 shows the range of
occupations in which they were employed. The most common occupational
category is sales assistant, accounting for approximately one-fifth of
this group. This is closely followed by labourers and factory and farm
workers, also with approximately one-fifth of workers, and
administration/clerical workers, who make up just under one-fifth. The
remaining young people are dispersed across a range of categories,
including food/hospitality employees, semi-skilled workers, marketing
and call-centre workers, and paid child and other personal carers.
Female respondents were more likely to be working in sales,
administration and food and hospitality jobs, while male respondents
were more likely to be in labouring, factory and farm jobs or
trade-related areas. In assessing the quality of the transition for this
group, it may be noted that these jobs are not, in general, occupations
requiring skills or qualifications, and most are low paid.
Further examination of these young workers indicates that a
significant proportion of them are not in full-time paid work. Table 10
shows the weekly hours worked by young people who are not in education
or training. For the 14.5 per cent of respondents who were working in
more than one job, their hours in different jobs have been combined.
Overall, the data shows that only 70.2 per cent of these young people
may be considered to be working the equivalent of a full-time load.
Moreover, there is a strong gender difference. While nearly four in five
young males are working a full-time load, fewer than two-thirds of young
females are in the same position.
[FIGURE 3 OMITTED]
Other measures of the stability and quality of the employment of
these young people were also provided in the survey. For example, nearly
six in ten of the respondents (57.3 per cent) indicated that their
current job was either a new one or was not the same as the one they
were doing when first surveyed in 2007. In addition, nearly one-quarter
of these respondents had worked in two or more jobs since the last
interview, while over three in ten reported that they were now looking
for a new or additional job. These data raise questions about the
stability and continuity of the occupational options available to these
young people.
Other measures collected in the survey include the
respondents' reported satisfaction with the job, their perception
of the job as a future career and the levels of formal and informal
training provided in the workplace (see Table 11).
These results provide a mixed picture of the employment situation.
Only four in ten saw their job as a potential future career, and only
three in ten reported a high level of satisfaction with their job.
Moreover, while the majority had received informal training in their
employment (defined as being shown how to do tasks or watching others),
only three in ten had received formal training (defined as seminars,
workshops, presentations or other kinds of training organised by
employers).
Of those respondents not in education or training, those who were
unemployed formed a very small part--only 8 respondents. The data
provided by these young people must therefore be treated with some
caution but the reasons they give for their difficulty in finding work
are illustrative of one of the main challenges facing young people: the
lack of skills and qualifications (see Table 12).
A small group of respondents fell into the category of being
neither in education or training, nor in the labour market (not in paid
work and not looking for paid work). These young people accounted for
0.5 per cent (4) of all respondents. Two were male and two were female.
All four reported not being ready for any more study at the moment.
Concluding remarks
This study was based on the premise that regional (or
non-metropolitan) school completers are much more likely than their city
counterparts to defer an offer of a place at university. Previous
research suggests that this is due to a combination of factors relating
to isolation and financial hardship (DETA, 2007; Polesel & Teese
2006;Teese, Clarke & Polesel, 2007) and this study confirms that,
compared with the broader population of school completers who defer,
many rural deferrers come from lower socio-economic backgrounds.
Despite these hardships, the respondents in this study display a
range of mainly positive destination outcomes. Approximately seven in
ten have taken up a place at university. A further 9.3 per cent have
entered vocational education and training courses, mainly at Certificate
IV level or above, and a further 3.1 per cent have entered traineeships
or apprenticeships. In all, over eight in ten are in some form of
education or training. Past research suggests that non-metropolitan
deferrers may in fact be more likely to take up their deferred place one
year on than their metropolitan peers (Teese, Clarke & Polesel,
2006), though it should be remembered that non-metropolitan students are
far more likely to defer in the first place. The current study also
provided some evidence that most of those young people who had entered
education or training were satisfied with their study choice.
Of the remaining 17.7 per cent of respondents, most were working
(16.3 per cent) and only a very small group (1.0 per cent) was
unemployed, while an even smaller group may be classed as inactive, that
is neither in education or training, nor working, nor seeking work. In
assessing the quality of the labour market experience for those in work,
it should be noted that most do not see their job as a longer term
career and few have received any formal training in that job. Work tends
to be in low status and poorly paid occupations--and much of it is part
time. Clearly, for this small but important group who do not take up
their place at university, the transition to the labour market has not
been ideal.
This research also suggests that some broad groups of deferrers in
country Australia are less likely to take up a university place than
others. These include those students whose achievement profile is low
and those who come from a background with lower socio-economic status.
Given that youth with a low socio-economic status are dominant amongst
rural deferrers in any case, it is somewhat disturbing that this
background characteristic continues to exert an influence. For these
groups, financial barriers exert a persistent influence. The costs of
travel, the costs of living away from home and the costs of study itself
present insuperable barriers and it seems that existing support schemes,
such as Youth Allowance, are not adequate to overcome them.
More importantly, perhaps, the study suggests that regional
disadvantage, as manifested in the phenomenon of deferral, also needs to
be carefully considered in other Australian states. It could be argued
that regional disadvantage is likely to be an issue of greater
significance and magnitude in the larger states where the 'tyranny
of distance' exerts a much more profound influence. If a
non-metropolitan home address presents as great a barrier to university
entry as it does in Victoria (at least in the immediate post-school
period), how much greater will that effect be in remote areas of
Queensland or western New South Wales?
The study also suggests that the transition to university may be
delayed rather than prevented altogether for regional school-completers.
Although the significant financial barriers already noted do not fall
away for all students, the study presents some evidence that they may
tend to fall away for some young people a year after school completion,
perhaps as a result of increasing financial independence gained through
work or the prospect of eligibility for Youth Allowance. The impact of
the delay itself has not been studied and its effects are largely
unknown. Does it disadvantage rural students relative to their
non-deferring peers to delay their graduation from university for a
year? Or does it perhaps give them an advantage in allowing greater
maturity before entry to their course?
It is important, therefore, to consider whether the more positive
outcomes reported in this study can be maintained over the longer term.
Past research suggests that one-fifth of deferrers who take up their
offer of a university place one year after deferring have dropped out
within a year, though the research does not distinguish between
metropolitan and non-metropolitan deferrers (Mason, Lamb & Polesel,
2007). Future contact with the cohort, scheduled for 2009, will allow an
assessment to be made of whether those students entering courses at
university or at a VET provider have continued in their studies or, in
the case of shorter courses, completed them. It will also allow a
consideration of what additional challenges rural students face in
finding or paying for accommodation when university entry requires them
to move away from home. The next contact will also consider whether the
long hours of work reported by some of the students in this study
reflect unsustainable financial pressures. It will allow us to assess
whether those who entered apprenticeships or are trainees are still in
training or have completed it. And finally, for those in less secure
destinations, particularly the unemployed, those in part-time work and
those who are inactive, the contact will provide information on whether
progress has been made in securing paid full-time work or a place in
education or training.
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Teese, R., Robinson, L., Lamb, S., & Mason, K. (2006). The 2005
on track longitudinal survey: the destinations of 2003 school leavers
two years on. Melbourne: Victorian Department of Education and Training.
University of Ballarat (2007). Submission to the 'Review of
the impact of the higher Education Support Act 2003'. Ballarat:
Planning, Quality and Review, University of Ballarat.
University of Melbourne (2007). Deferment Policy. Retrieved 13
January, 2009, from http://www.services.unimelb.edu.au/policy/downloads/Deferment_Policy.pdf
Western Research Institute (2004). Are enterprise zones a tool for
reducing regional disadvantage in Australia. Paper presented to the
Bureau of Transport and Regional Economics Regional Research Colloquium.
Retrieved 11 August, 2008, from www.bitre.gov.
au/publications/91/Files/t_murphy.pdf
John Polesel
University of Melbourne
John Polesel is Principal Research Fellow at the Centre for
Post-compulsory Education and Lifelong Learning at the University of
Melbourne. Email: jpolesel@unimelb.edu.au
Table 1 Growth in deferral rate (metropolitan
and non-metropolitan Victoria) 2004, 2007
Year Metropolitan Non-metropolitan
2004 5.5% (n = 1332) 9.9% (n = 541)
2007 6.5% (n = 1631) 15.9% (n = 1403)
Source: Author's analysis of DEECD data
Table 2 Designed and achieved sample: 2006 Year 12 non-metropolitan
deferrers
Organisation Deferrals Recruited to
study
Baw Baw Latrobe LLEN 69 69
Campaspe Cohuna LLEN 31 30
Central Grampians LLEN 24 24
Central Ranges LLEN 63 60
Gippsland East LLEN 68 61
Goldfields LLEN 148 137
Goulburn Murray LLEN 94 90
Highlands LLEN 126 125
NE TRACKS LLEN 79 77
North Central LLEN 6 6
Northern Mallee LLEN 37 37
South Gippsland Bass Coast LLEN 62 60
South West LLEN 95 93
Wimmera Southern Mallee LLEN 28 28
Total 930 897
Organisation Surveyed Surveyed as %
of cohort *
Baw Baw Latrobe LLEN 62 89.9
Campaspe Cohuna LLEN 28 90.3
Central Grampians LLEN 20 83.3
Central Ranges LLEN 53 84.1
Gippsland East LLEN 55 80.9
Goldfields LLEN 119 80.4
Goulburn Murray LLEN 76 80.9
Highlands LLEN 112 88.9
NE TRACKS LLEN 73 92.4
North Central LLEN 5 83.3
Northern Mallee LLEN 32 86.5
South Gippsland Bass Coast LLEN 56 90.3
South West LLEN 90 94.7
Wimmera Southern Mallee LLEN 25 89.3
Total 806 86.7
* Cohort is defined as 2007 DEECD tracking respondents
identifying as deferrers.
Table 3 Selected characteristics of survey sample and all deferrers
from 2006 Year 12 cohort
Survey sample
(%) n = 806
Gender Male 41.9
Female 58.1
Achievement Lowest quartile 13.8
Next lowest quartile 23.0
Next highest quartile 37.3
Highest quartile 25.9
Socio-economic Lowest quartile 44.8
status Next lowest quartile 37.2
Next highest quartile 15.8
Highest quartile 2.2
All deferrers
(%) n = 3036
Male 41.9
Female 58.1
Lowest quartile 14.7
Next lowest quartile 23.2
Next highest quartile 32.6
Highest quartile 29.5
Lowest quartile 25.6
Next lowest quartile 25.8
Next highest quartile 25.1
Highest quartile 23.5
Table 4 Main destinations in 2008
Detailed destination Males
n %
University (degree level) 240 71.0
VET Certificate IV+ 20 5.9
Entry-level VET 5 1.5
Apprenticeship 6 1.8
Traineeship 3 0.9
Full-time paid work 46 13.6
Part-time paid work 13 3.8
Unemployed 3 0.9
Inactive 2 0.6
Total 338 100.0
Detailed destination Females
n %
University (degree level) 323 69.0
VET Certificate IV+ 38 8.1
Entry-level VET 12 2.6
Apprenticeship 4 0.8
Traineeship 12 2.6
Full-time paid work 46 9.8
Part-time paid work 26 5.6
Unemployed 5 1.1
Inactive 2 0.4
Total 468 100.0
Detailed destination Total
n %
University (degree level) 563 69.9
VET Certificate IV+ 58 7.2
Entry-level VET 17 2.1
Apprenticeship 10 1.2
Traineeship 15 1.9
Full-time paid work 92 11.4
Part-time paid work 39 4.8
Unemployed 8 1.0
Inactive 4 0.5
Total 806 100.0
Table 5 Study and labour market destinations in 2008
University degree VET
n % n %
Not in the labour force 205 36.4 13 13.0
Apprentice/trainee 0 0.0 25 25.0
Full-time paid work 11 2.0 10 10.0
Part-time paid work 269 47.7 41 41.0
Unemployed 78 13.9 11 11.0
Total 563 100.0 100 100.0
Not in education All
or training
n % n
Not in the labour force 4 2.8 222
Apprentice/trainee 0 0.0 25
Full-time paid work 92 64.3 113
Part-time paid work 39 27.3 349
Unemployed 8 5.6 97
Total 143 100.0 806
Table 6 University entry and socio-economic status
Lowest Next lowest Next highest
quartile quartile quartile
University (%) 66.8 69 78.7
n 241 207 100
Not university (%) 33.2 31.0 21.3
n 120 93 27
Highest Total
quartile
University (%) 83.3 69.9
n 15 563
Not university (%) 16.7 30.1
n 3 243
Pearson Chi-Square value of 8.061 (2-sided asymp. sig. 0.045) n = 806
Table 7 University entry and achievement
Lowest Next lowest Next highest
quartile quartile quartile
University (%) 40.5 57.3 75.7
n 45 106 227
Not university (%) 59.5 42.7 24.3
n 66 79 73
Highest Total
quartile
University (%) 88.0 69.9
n 184 562
Not university (%) 12.0 30.1
n 25 243
Pearson Chi-Square value of 96.703 (2-sided asymp. sig. 0.000) n = 806
Table 8 Institutions of respondents by location
Provider type N %
Metropolitan VET providers 50 54.9
Non-metropolitan VET providers 39 42.9
Interstate VET providers 2 2.2
Total VET 91 100
Metropolitan universities 333 58.6
Non-metropolitan universities 199 35.0
Interstate universities 36 6.4
Total university 568 100
Note that the numbers reported in this table do not align exactly with
the number of degree-level and VET students reported in Table 4. This
is because students in a university may be enrolled in Certificate or
Diploma level programs, and conversely, students in a TAFE may be
enrolled in degree-level programs.
Table 9 Paid working hours (weekly) of university
and TAFE students
Hours of paid work n %
10 hours or fewer 174 50.9
11-20 hours 125 36.5
21 hours or more 43 12.6
Total 342 100
Table 10 Hours worked per week by respondents
not in education or training
Males % (n = 59) Females % (n = 72)
Fewer than 35 hours 22.1 36.2
35 hours or more 77.9 63.8
Table 11 Perceptions of job by working
respondents not in education or training
Perception Percentage of respondents
(n = 131)
Would like this type of job as career 40.5
Very satisfied with this job 29.8
Have had formal training in this job 32.1
Have had informal training in this job 80.9
Table 12 Reasons for difficulty finding a job (n = 8)
Perception Percentage of respondents
Not enough or appropriate skills or training 75
Not enough or appropriate qualifications 75
Not enough job experience 62.5
Not enough jobs available 62.5
Need to move away from home 37.5
Health problem or disability 12.5
Figure 1 Distance of study location from home
<50 km (14.7%)
150 km or more (61.9%)
100-149 km (14.2%)
50-99 km (9.2%)
Note: Table made from pie chart.
Figure 2 Main reasons for not taking up study in 2008
Have found something else 58
Wanted to travel/take gap year 49.7
Not ready for any more study 44.1
Too difficult to support myself 43.4
Costs of study are a barrier 39.2
Meant leaving home 33.6
Course not offered locally 30.8
Financial pressure on family 27.3
Not worth building HECS debt 26.6
Costs of travel are a barrier 25.9
Too much travel involved 23.8
Unable to get preferred course 21.7
Unsure could cope with the work 17.5
Only got into fee-paying course 13.3
Never wanted to go to university 9.1
Waiting for Youth Allowance 7.7
Never intended to study 7.7
Note: Table made from bar graph.
