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Demographic and Social Change:Implications for Education Funding.Authors: Phil Aungles May 2000 ã Commonwealth of Australia 2000 ISBN 0 642 44471 4 (Online version) This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the AusInfo. Requests and inquiries concerning reproduction and rights should be addressed to the Manager, Legislative Services, AusInfo, GPO Box 84, Canberra ACT 2601.AcknowledgementThe authors are grateful for helpful comments on the paper received from Les Andrews, Peter Karmel and William Thorn. The views expressed in this paper do not necessarily reflect the views of the Department of Education, Training and Youth Affairs ContentsPart I: An Ageing PopulationPart II: Participation in EducationPart III: Education ExpendituresPart IV: Social ExpendituresPart V: ConclusionAppendixIntroductionImplications for Education FundingThis paper identifies the likely implications of major demographic change and changes in educational participation for education funding in the longer term. In particular, the paper focuses on the ageing population, changes in educational participation and the growth of social expenditures as potential key pressures on the funding of the education sector to the Year 2021. The purpose of the paper is not to draw definitive conclusions about the future size and role of the education sector. Rather, the intention is to explore current issues and trends in this area and the environmental pressures within which policy may need to operate. The first section of the paper discusses the trend towards an ageing population. Ignoring other changes this will lessen pressures for funding education because students are concentrated in the younger age groups. The second section considers likely trends in educational participation. The paper suggests that current levels of access to tertiary education are very high and maybe close to universal. Nevertheless increasing duration of study and lifelong learning could have important implications for future trends in participation. The third section draws on the earlier findings relating to the ageing population and trends in educational participation to explore likely longer term developments in education expenditure. In the fourth section, trends in education expenditures are placed in the broader context of likely longer term changes in social expenditures. Policy pressures in education depend not just on the impact of ageing on the education sector but more broadly on whole of government developments. The major themes and conclusions of the paper are presented in the final section.Part IAn Ageing PopulationAustralia is projected to have a more middle aged and older population over the next twenty five years. The major factors driving changes in the population and demographic structure over this period are declining fertility and mortality rates[1]. In the past 20 years the demographic structure has shifted away from younger age groups to middle-aged and older age groups (Figure 1). Over the next 25 years, the age distribution of the population shifts towards older age groups at the expense of younger age groups with little change in the relative size of middle-aged cohorts. A way of highlighting the impact of the changing age distribution for future education and social expenditures is to consider the dependency ratio (Figure 2), that is the number of younger (0-14 year-olds) and older (65+ year-olds) persons expressed as a percentage of persons of workforce age (15-64 year-olds). The dependency ratio is shown for the period 1976-2021. The total ratio broadly declines until around 2010 and then increases. However, the decomposition between ‘young’ and the ‘old’ suggests that governments are likely to focus increasingly on the social expenditures relating to the elderly relative to education expenditures. Figure 1: Age distribution of the population, 1976, 1996 and 2021 
Figure 2: Dependency ratio, 1976 - 2021 
The change in the demographic structure implies education expenditures are likely to fall, all other factors held constant, given the present age profile of students enrolled in education institutions. Figure 3 below shows students are largely drawn from younger age groups, though this is less the case for TAFE students. School students are obviously concentrated in younger age groups while the majority of higher education students, 59 per cent, are aged under 25. The majority of TAFE students, 53 per cent, are under 30 years of age. Figure 3: Age distribution of students, 1996 
Changes in the regional distribution of population represent a further pressure on education expenditures. The extent of population change over the next twenty five years will vary by State and Territory. For instance, between 1996 and 2021 population growth is expected to be fastest in Queensland (2.0 cent annual average growth), followed by Western Australia (1.7 cent) and the Northern Territory (1.5 cent)[2]. Other States will experience slower than average population growth (1.1 per cent) with Tasmania projected to show very low growth (0.2 per cent), compared to 0.4 per cent in South Australia and 0.6 per cent in Victoria. While the growth projections are all positive, projections of higher education student demand (that is, an age weighted projection) are negative in Victoria, South Australia, the Australian Capital Territory and Tasmania (Figure 4). Figure 4: Projected demographic impact on demand for undergraduate places by State/Territory (Index 1995=100) Figure 4: Projected demographic impact on demand for undergraduate places by State/Territory (Index 1995=100)
[1] Population projections are based on the ABS Series A population projections which assume lower fertility and mortality and relatively lower overseas migration, ABS, Projections of the Populations of Australia, States and Territories, 1995-2051, 3222.0. [2] The ABS Series A State population projections assume medium level interstate net migration to the year 2000-01 and no change thereafter. Part IIParticipation in Education
Two key determinants of education expenditures over the longer term are the pool of potential students and the rate at which they participate in education. The first section demonstrated demographic pressures are likely to lead to lower pressure on education expenditures assuming the current age profile of students is maintained in the future. This section discusses trends in school retention rates and likely developments in tertiary participation. School retention ratesSchool participation, as measured by the Year 12 apparent retention rate, increased very rapidly during the 1980s. The apparent retention rate more than doubled in that period reaching a peak of 77 per cent in 1992. This increase in the apparent retention rate has been associated with large shifts in the full-time labour market activity of teenagers. This is to be expected since for teenagers there is a choice after completing compulsory schooling between continuing to Year 12 or seeking full-time work. In 1978, 41 per cent of the teenage population worked full-time whereas in 1996 some 17 per cent were employed full-time. These changes have also, in part, been associated with an increase in the proportion of teenagers working part-time, from 10 to 29 per cent over the same period. As teenagers have increasingly stayed on at school it appears that employment is still seen as an important activity with 30 per cent of school students engaged in part-time work. Current trends, however, are unlikely to reverse given they are largely driven by long term structural changes associated with increased labour force participation of older women and changes in the occupational demand for labour, in particular the growth in demand for professionals and para-professionals. Predicting the Year 12 apparent retention rate in 25 years time is extremely difficult and unlikely to be a very worthwhile or sensible exercise. However, there is considerable variation in the Year 12 apparent retention rates among the States and the male apparent retention rate is currently 12 percentage points below the female apparent retention rate. Therefore, there is considerable scope for overall retention to increase. Tertiary participationIn the remainder of this section we present a more extensive consideration of trends in tertiary participation. For methodological reasons, we are chiefly concerned with post-school participation but on occasion we examine higher education and TAFE participation separately. Participation in tertiary education might increase in the future for two reasons. Either a greater number of students could gain access to tertiary education or persons may study for longer (or access and duration may increase simultaneously). This paper assesses likely trends in access to tertiary education through a cohort approach, that is, by looking at the entry of age cohorts to education. Combined with a brief discussion of the duration of study this will give some idea of prospective trends in tertiary participation. Access to higher educationThis paper uses a life table approach to explore the access of age cohorts to tertiary education. We observe the behaviour of different age cohorts over a relatively short span of time and assume these patterns are unchanged over the life cycle. By inference, we can then assess the likelihood that members of cohort will attend tertiary education. We have two ways of approaching this task. The first approach is to use administrative data from the higher education and TAFE sectors showing entry to tertiary education by age. An alternative way of proceeding is to examine ABS data on educational qualifications by age. The advantage of using the two approaches is that each approach can check or corroborate information provided by the other. From the higher education database we obtain the number of commencements in higher education in 1995[1] of persons who had no prior experience of university study by single year of age. We then obtain the probability of persons entering higher education in that year for the first time for each age group by dividing by the respective population. By summing these ‘age commencement’ probabilities we derive an estimate of the likelihood of a person entering higher education throughout their lifetime. Figure 5 shows that most persons attend university at a relatively young age. By age 25 it is estimated that about 36 per cent of any particular cohort will have entered higher education. Thereafter, an additional 9 per cent of the cohort will enter university giving a lifetime probability of attending university of about 45 per cent. Figure 5 shows that most persons attend university at a relatively young age
The estimates presented below do not include persons who failed to provide information about their prior education experience. Hence the estimates represent a lower bound to lifetime university attendance. If this additional group of persons were included then the likelihood of entering university by age 25 would rise to 38 per cent and over a lifetime to 50 per cent. This represents an upper bound to access to university since some of these persons may already have been to university. The lifetime probability of attending university now appears to have attained a higher level, at around 45 per cent, compared with comparable figures in the late 1980s and early 1990s (Figure 6). Thus it appears increasing access has contributed to increasing participation in higher education over the 1990s. However, there was some variation in estimates of lifetime access in the late 1980s and early 1990s. For example, in 1992 the estimated lifetime probability of attending higher education fell 4 percentage points to 38 per cent. This indicates the steady increase in enrolments or participation in the 1990s has not necessarily resulted in increasing access to university. Thus since participation in higher education depends on both access and the duration of study it follows that students must have engaged in longer periods of study in some years during the 1990s. Relevant factors here include a decline in the number of two year diploma awards, an increase in the numbers undertaking a postgraduate degree and an increase in the popularity of double degrees. The issue of changes in the duration of study is examined later in this section. Figure 6: Lifetime probability of attending higher education and student enrolments, 1989 - 1997
An alternative approach to predicting the lifetime probability of attending higher education is to track cohorts through time using ABS educational attainment data. For example, we can examine the proportion of 15-19 year-olds with degrees in 1991 and five years later in 1996 measure the proportion of 20-24 year-olds with degrees. The difference between these two figures gives the likelihood of 20-24 year-olds attending university and getting a degree over the 5 year period. We can follow a similar procedure for all other age cohorts up to age 65. We then estimate the lifetime probability of getting a degree by summing the probability of completing university over a 5 year period for each of the age groups. Figure 7 shows that, using ABS data, the lifetime probability of getting a degree is around 41 per cent (the earlier graph using administrative data is included as a comparison). However, the ABS data have not been adjusted to allow for the influx of educated migrants (that is, the 1996 data will include migrants entering Australia between 1991 and 1996). The ABS estimates of the lifetime probability of getting a degree are therefore inflated by the impact of migration. Note the administrative data refer to commencements and the ABS data refers to completions. Comparing the two data sources gives a relatively high completion rate of around 90 per cent but note this figure is probably biased upwards because of the effect of migration in the ABS figures. Urban et al. (1999) estimate the completion rate for the cohort of undergraduates commencing study in 1992 at around 82 per cent. Applying this to the ABS qualifications data gives a lifetime probability of attending higher education of around 50 per cent, a little higher than the estimate from administrative commencement data. One noticeable feature of Figure 7 is the flatter age profile of the ABS data. This may be explained, in part, by persons entering university but then completing their degree with some time delay. Figure 7: Lifetime probability of commencing and completing higher education, based on 1995 data
It would appear from the two data sources that we can draw a broad conclusion that the lifetime probability of entering higher education appears to be 45 to 50 per cent, which places Australia among the top ranking OECD countries in terms of access to higher education. The approach taken above assumes the behaviour of different age cohorts at the present point in time is repeated from here on. Thus it makes no allowance for the fact that a sudden increase in the number of younger persons entering university might flow through to a lower probability of attendance later on in life. Given substantial increases in higher education participation by younger age groups in recent years then, following this line of argument, the estimates presented above may represent an overestimate of the lifetime probability of attending university. There is a contrary argument. That is, recent increases in participation by younger age cohorts may well lead to a greater ‘taste’ for education. As particular age groups see more and more of their peers entering university this might encourage others within that age group to do likewise. Under this scenario, there is no reason to suspect any drop off in the number of new entrants to university. The sizable male/female differential is also worth noting. Analogous calculations to those in Figure 6 give estimates of probability of attending higher education of 38.1 per cent for males and 52.5 per cent for females. Any trend for male participation to catch up to female participation would directly lead to an increase in overall participation. Access to vocational educationWe use the same life cycle approach to investigate lifetime TAFE attendance using administrative data from NCVER. We include only persons new to TAFE in 1995 with no stated prior tertiary education experience undertaking the following courses; diploma through to trade certificate and AQF Certificate III through to AQF Bachelor’s Degree. Figure 8 shows that most persons entering TAFE for the first time do so at a younger age. However, unlike higher education, there are also a significant number of older persons entering TAFE who are new to tertiary education. By age 20 the likelihood of entering TAFE is about 27 per cent and this rises to 34 per cent by age 25. Thereafter, the number of older persons entering TAFE rises steadily so that in total about half of each cohort is projected to enter TAFE. That is, the lifetime probability of entering TAFE is estimated to be about 52 per cent. This is likely to be an overestimate of lifetime access to TAFE since the data refer to enrolments rather than the number of persons commencing in TAFE. That is, the data include persons undertaking multiple enrolments. However, given the nature of the courses selected eg diplomas, trade certificates and the like which tend to be of longer duration then the likelihood of multiple enrolments may be fairly limited[2]. Since there is a possibility of the inclusion of multiple enrolments and we have included enrolments in only a selected range of TAFE courses, it must be acknowledged there is a certain amount of imprecision attached to the estimates of lifetime access to TAFE presented here but they are, nevertheless, intended to be broadly indicative. Figure 8: Lifetime probability of attending TAFE, based on 1995 data
The same point about recent increases in access to higher education by younger age groups possibly flowing through to a lower likelihood of entry later in life also applies to TAFE. The contrary argument that this might alternatively lead to an increasing ‘taste’ for TAFE education later on in life could also apply. Estimates of lifetime access to TAFE using ABS data are not shown above. The data are unable to show the number of persons newly acquiring TAFE qualifications in each age group. The educational attainment data are net figures in effect because they show the number of persons newly acquiring TAFE qualifications but exclude persons moving on from TAFE to higher education. Given that we are unable to reliably account for the latter group we have not presented estimates of TAFE participation based on ABS data above. Access to tertiary participationIf we sum the proportion of persons new to higher education and new to TAFE we can estimate the lifetime probability of entering tertiary education. However, before we do this we need to exclude persons entering higher education who have had prior tertiary experience in the TAFE sector and vice-versa. In the earlier discussion it was shown the lifetime probability of entering higher education in 1995 was around 45 per cent using administrative data. If we exclude all persons with prior TAFE experience that figure falls to around 38 per cent and this is the appropriate figure shown in Figure 9. The figure shows that using the life cycle approach the estimated lifetime probability of entering tertiary education is very high. Our estimates using administrative data suggest that almost 90 per cent of persons can expect to enter higher education or TAFE over their lifetime. This would tend to suggest that access to tertiary education must be very close to saturation point providing little scope for further increases in terms of access. If we include new entrants to higher education who failed to indicate whether they had any prior education experience then we derive an upper bound for access to tertiary education of 94 per cent. Lifetime probability of commencing and completing tertiary education, based on 1995 data Figure 9: Commencing tertiary (based on higher education and TAFE administrative data)
Figure 10: Completing tertiary (based on ABS educational qualifications data)
We use the ABS educational attainment data, showing persons with post-school qualifications, to estimate that the lifetime probability of completing a tertiary qualification is around 70 per cent as shown in Figure 10[3][4]. However, as was mentioned earlier, the attainment data refer only to completions and excludes persons who commence a course but fail to complete. Comparing the commencement data from administrative sources with the completions data from ABS estimates gives a crude estimate of the completion rate in tertiary education of 76 per cent, compared to Urban et al.’s estimate of 82 per cent for higher education noted earlier. Once again, this figure may be biased upwards to the extent that there is no allowance made for the impact of educated migrants in the methodology using ABS estimates. In any event there appears to be relatively little scope for further significant expansion in the proportion of persons accessing tertiary education (participation is a different matter since it also depends on length in education). Duration of higher educationWhile there appears to be limited scope for further substantial increases in access to tertiary education, participation could continue to increase if students pursue further study or courses requiring longer periods of study. With regard to the latter point, the growing popularity of double degree courses might be one source of increasing participation (about 5 per cent of the student population were enrolled in double degrees in 1997). We can derive estimates of the duration of study by applying the same cohort approach used before. First, we derive lifetime participation in higher education for all participants, not just commencers. That is, we sum age participation rates for all first year, second year, third year students and so on. Next we divide this sum by the earlier estimate derived for the lifetime probability of accessing university (45 per cent in 1995). Dividing lifetime participation rates for the whole student population by lifetime access rates for newly commencing students gives an estimate of the average number of years that a cohort can expect to attend university. In 1997 this figure is 3.8 years. It is interesting to observe that the average duration of study is only marginally higher at the end of the period under observation compared with the starting point. Duration increased steadily during the early 1990s as existing students stayed on longer and this may, in part, be due to the impact of the recession. Thus participation increased in the early 1990s as universities accommodated their existing students’ desire for more lengthy periods of study. While duration may be influenced by shorter run cyclical factors, there may also be structural factors at work such as the trend towards double degrees and lifelong learning. These structural factors are acting to increase duration, and therefore participation, over the longer term. Offsetting these factors are policy settings affecting the level of HECS (rates increased in 1996 for new students) and the reduction in HECS liable places for post-graduate coursework. Figure 11: Duration of study and student enrolments in higher education, 1989-1997 Figure 11: Duration of study and student enrolments in higher education, 1989-1997
If we compare Figures 6 and 11 we note that the sources of increasing participation differed over the period. During the early 1990s, while there was some variation in access to higher education as shown by Figure 6, there was nevertheless a consistent upward trend in duration as universities enabled established students to study longer. From, the mid 1990s onwards, this pattern changed as universities permitted greater numbers of new entrants access to higher education while at the same time duration fell, returning to levels close to those of the late 1980s. [1] We use 1995 as the base year since this is the latest year for which comparable higher education and TAFE data are available. [2] There were 135,800 enrolments in diplomas, trade certificates etc which was the focus of access to TAFE in this paper (unpublished data, National Centre for Vocational and Education Research). Associate diplomas have a likely duration of two years full-time, AQF Certificates III have a likely duration of a year, while the Advanced Certificates and Trade Certificates have a likely duration of less than a year, but nevertheless involve a substantial period of education. There were a further 284,900 enrolments in lower level TAFE courses including Certificate - Not elsewhere classified, Endorsement to Certificates, Statement of Attainment, Certificate of Competency, Certificate of Proficiency, AQF - Senior Secondary and Certificates I and II and Other qualifications not including non-award courses. Given the lower level courses tend to be of shorter duration, it is expected that the likelihood of multiple enrolment in these courses will be higher than for the higher level courses which are the focus of this paper. [3] Higher education and TAFE data are not shown separately in Figure 10. The higher education data from ABS sources does not refer exclusively to persons new to tertiary education. Persons may have attained a degree following on from an earlier TAFE qualification. Therefore only persons completing tertiary qualifications are shown to avoid the problem of double counting. [4] There is a slight problem with the ABS data for older age groups because it suggests the proportion with TAFE qualifications fell between 1993 and 1996 (consistent data are only available for the shorter time period). As a result, the lifetime probability of attending tertiary education actually falls at older ages by a couple of percentage points. We are unsure of the precise reasons for this phenomenon. However, it doesn’t substantially affect our overall conclusion that lifetime access to tertiary education is very high and close to saturation point. For this reason, we prefer to show the lifetime probability of completion of a tertiary award as levelling out rather than declining at older ages using the ABS data. Part IIIEducation Expenditures
School expendituresWe project changes in school expenditures on the basis of demographic trends to the Year 2021. For the base case scenario, presented in Figure 12, it seems reasonable to assume the Year 12 apparent retention rate remains unchanged to the Year 2021. The scenarios presented for school expenditures both assume no change in the mix of Commonwealth, State and private funding of school expenditures. The issue of the mix of school expenditures is considered beyond the scope of the present paper. Overall, growth in the student population and changes in living standards are expected to lead to real growth in school expenditures of 46 per cent to the Year 2021. We assume all social expenditures, including those on schools, rise in line with living standards. The idea here being that higher living standards, as measured by GDP per capita, enable the community to increase its call on resources in each sector. (For example, it is reasonable to assume that teachers’ salaries move in line with overall increases in real wages.) However, as noted in the earlier discussion on population trends, the youngest age cohorts are expected to diminish in importance. Consequently, the community’s calls on resources for the schools sector is likely to decline over the longer term as shown by Figure 12 from 4.1 per cent of GDP in 1995-96 to 3.4 per cent of GDP by the Year 2021. These trends assume a continuation of present policy settings. However, government or community may well decide to change the allocation of resources to schools in favour of or at the expense of other sectors of education or indeed over other sectors of the economy. Figure 12: School expenditures as a percentage of GDP, 1995-96 to 2020-21
In an earlier section it was noted the Year 12 apparent retention rate had declined somewhat in recent years though it was still at a relatively high level compared to historical trends. On this basis, it did not seem reasonable to project any large increase in retention or school participation over the projection period. As an alternative scenario it was assumed the Year 12 apparent retention rate in all States increased in line with the highest apparent retention rate in Queensland of 77.9 per cent, relative to the base case assumption of 72.2 per cent.[1] This makes only a marginal difference to real growth in school expenditures which grow by an additional 1 per cent in total over the projection period. Higher education expendituresWe project future higher education expenditures largely on the basis of population trends and present policy settings as a way of assessing future directions in the sector. Key factors are recent trends in higher education funding, the ageing of the population and the shift to greater private funding. Overall, population growth and changes in living standards are expected to lead to real growth in higher education expenditures to the Year 2021. However, the ageing population implies the higher education sector’s claim on resources is likely to decline over the longer term as shown by Figure 13. However, the projections are based on a continuation of present policy settings. Decisions to allocate additional resources to lower student-staff ratios or increase expenditure per staff member might have the effect of negating the longer term decline in resources flowing from the ageing population. Note that the estimates presented here include AUSTUDY payments to higher education students, which strictly speaking do not represent a call on resources by the higher education sector. These payments may be spent on food, clothing, housing and so on and thus represent a claim on the resources of sectors other than higher education. However, for the purposes of this paper they are included to show the expenditure of government outlays on the higher education sector. We follow a similar approach for other social outlays elsewhere in the paper. In the near term, higher education expenditures as a share of GDP are projected to fall from 1.3 % in 1995-96 to 1.1 % at the turn of the century by 2000-01, reflecting recent funding decisions. Beyond that, the slower growth of younger age cohorts is projected to lead to higher education expenditures falling as a share of GDP to 1.0% by the Year 2021. Figure 13: Higher education expenditures as a percentage of GDP, 1995-96 to 2020-21
Figure 14 provides an historical context for this projection.[2] As can be seen from the figure, total higher education expenditure relative to GDP grew very substantially over the 1960s and 1970s, dipped somewhat over the 1980s before increasing in the early 1990s. The public/private mix has also changed significantly with the removal of fees with the Whitlam government in the 1970s and the introduction of Higher Education Contributions Scheme (HECS) in the late 1980s. Figure 14: Higher education expenditures as a percentage of GDP (1962-1996)
Source: Karmel (1999) Higher education fundingWhile the demand for education influences total education expenditures, another key issue for trends in government expenditures in this area is the distribution of public and private funding of education. This issue is particularly relevant to higher education expenditures given that most students make payments under the Higher Education Contributions Scheme (HECS) and the recent introduction of undergraduate fee-paying places for domestic students at some universities. Private expenditures on higher education, as a proportion of GDP, are projected to rise only marginally from 0.26 per cent to 0.28 per cent over the projection period as shown by Figure 13. The reason being that the effect of changes to increase private funding through the new HECS arrangements and introduction of fees are almost entirely offset by decreased calls on resources flowing from an ageing population. The base scenario assumes that private funding of universities through HECS payments steadily increases to about 30 per cent of total funding by the middle of the next decade. (This is less than the average 37 ½ per cent of funding that HECS is supposed to represent because the Commonwealth Government pays the 25 per cent discount for upfront fees, forgoes real interest and also meets HECS liabilities where student’s incomes are below the income threshold.) The base scenario also assumes the introduction of fee paying opportunities adds to private expenditures equivalent to 5 per cent of existing funding of institutions (from Commonwealth grants and HECS payments). Since private funding of higher education is virtually static to the Year 2021, the switch to private rather than public provision implies that Commonwealth government expenditures fall relatively rapidly to about 0.7 per cent of GDP by the end of the projection period. State government expenditures are a relatively insignificant component of higher education funding. Mature age participation doublesAs an alternative to the base scenario we assume a doubling of mature age participation, not to indicate that this might occur but rather, to demonstrate the orders of magnitude in terms of its impact on higher education expenditures. We find that this would have the effect of reversing the projected decline in higher education expenditures. Instead of falling from 1.30 per cent to 1.01 per cent of GDP, higher education expenditures would rise to 1.35 per cent of GDP by the Year 2021. Commonwealth outlaysIn Figure 15 we show the potential path of real Commonwealth outlays on higher education (this excludes HECS payments and other private outlays) to the Year 2021 under various scenarios. If we assume outlays increase in line with demographic trends beyond the forward estimates period, then there is a slight increase of 1 per cent in real outlays beyond 2000-01. (This rise in outlays is relatively small because the scenario also assumes HECS arrangements lead to a shift from public to private expenditures). However, over the entire period under this scenario real outlays are projected to fall by 4 per cent because of the 1996 Budget measures. Under the second scenario we assume real outlays increase in line with demographic trends and changes in living standards (per capita growth) beyond the forward estimates period. By the Year 2021 outlays are projected to rise by 18 per cent over base period outlays. The third scenario assumes real outlays increase in line with demographic trends, living standards and a doubling of mature age participation (beyond the forward estimates period). Under this scenario outlays are projected to increase much faster by 62 per cent over the period. Finally, we show as a benchmark the path of Commonwealth outlays if they were to rise in line with GDP. In this scenario outlays would be 71 per cent higher at the end of the projection period. This last benchmark scenario highlights the point that Commonwealth outlays on higher education are likely to grow more slowly than GDP. Figure 15: Commonwealth outlays on higher education, 1995-96 = 100 
Note : Scenario 2 refers to the base case, see Figures 17 and 18. TAFE expendituresIn projecting TAFE expenditures, we assume expenditures rise in line with demographic pressures and increases in living standards. Given that access to tertiary education is already very high, it seems reasonable to assume no change in TAFE participation rates in the base case scenario. Likewise, we assume existing policy settings are unchanged. Growth in the student population and rising living standards are projected to lead to real growth of 54 per cent in TAFE expenditures over the projection period. However, as a result of the ageing population, TAFE expenditures as a proportion of GDP are projected to decline from 0.63 per cent of GDP in 1995-96 to 0.57 per cent by the Year 2021. Given the older age profile of the TAFE population, the decline in the TAFE sector’s call on resources is not quite as marked as it is for the schools and higher education sectors. A continuation of trends in mature age participation in TAFE over the last decade would see mature age participation more than double over the projection period. Like the higher education sector then, our alternative scenario for the TAFE sector assumes mature age participation doubles over the projection period. Once again, this is not suggesting this increase in participation is likely to occur but rather to demonstrate the magnitude of the impact on TAFE expenditures. We find that instead of falling, TAFE expenditures as a share of GDP would increase to 0.93 per cent by the Year 2021. Figure 16: TAFE expenditures as a percentage of GDP, 1995-96 to 2020-21
Education expendituresOur base case scenario suggests education expenditures as a share of GDP are likely to fall from 6.0 per cent in 1995-96 to about 5.0 per cent by the Year 2021 as shown by Figure 17. Most of the decline is accounted for by the schools sector since this is substantially larger than the tertiary sector. This implies that even if trends to higher mature age participation in the tertiary sector were to eventuate, they would be unlikely to counteract pressures for lower education expenditures as a result of an ageing population. Figure 17: Education expenditures as a percentage of GDP, 1995-96 to 2020-21
[1] The base year for the projection is 1995/96. The school retention rate in 1995 was 72.2 per cent. The alternative scenario was based on the latest available data when the projection were prepared (1997), when the national apparent retention rate was 71.8 per cent. In 1999, the national retention rate was 72.3 per cent. [2] The historical estimates were based on earlier ABS estimates of education outlays. These estimates included net advances to students for HECS purposes in government outlays. However, gross advances (or liabilities) for HECS purposes were already included in private final consumption expenditure for the education sector. Thus the historical estimates represent overestimated higher education expenditures. From the 1996/97 edition of Expenditure on Education, 5510.0, net advances for HECS purposes were no longer included in government outlays but shown as a separate 'below the line' financing item. Part IVSocial Expenditures
An ageing population and greater private funding appear quite compelling reasons for the projected decline in government education expenditures and, in particular, higher education expenditures to the Year 2021. However, it is not sufficient to view education funding in isolation from all the other calls on resources that the community may wish to make. Indeed, technical change and changes in tastes and preferences are pervasive influences on the structure of the economy and government spending. In this view of the world, the demand for education resources competes with many other claims on resources. Thus the size and role of the education sector by the Year 2021 will be the end result of the interplay of much broader forces. The aim of the present section is to briefly outline some key trends in other social expenditures. Social expenditures generally refer to those items of expenditure that appear to have a varying impact on different age groups. For example, education expenditures directly benefit mainly younger persons. On the other hand, old age pensions, as the name implies, benefit older persons. Health expendituresThe health sector is currently not much larger than the education sector; in 1995-96 it accounted for about 8 per cent of GDP. However, the health sector is a key area because health costs are increasing more rapidly than any other area of social expenditure. Changes in medical technology, treatments and the like, particularly in areas such as pharmaceuticals and diagnostic testing, are leading to rapidly escalating costs in addition to the effects of an aging population. In an environment of rapidly increasing health costs it is difficult to make sensible projections about longer term changes in health expenditures. Current trends are clearly difficult to sustain in the absence of changes in community attitudes in support of higher health expenditures. The task therefore is to show possible pressures on the basis of pre-existing trends. This will enable judgements to be made about possible orders of magnitude of changes in the health sector relative to other areas of social expenditure or indeed any other expenditure. We assume in a base case scenario that per capita health costs for each age group increase by 2 per cent per annum[1]. This compares with the assumption in the base case scenario that all other education and welfare expenditures rise in line with growth in living standards (of around 1 – 1 ¼ per cent per annum). The 1996 Budget measures have temporarily curbed the growth of health expenditures as shown by Figure 18 below. Beyond the forward estimates, however, health expenditures are projected to increase from 8.1 per cent of GDP in 2000-01 to 11.1 per cent of GDP by the Year 2021, an increase of 3 percentage points in the space of 20 years. That this scenario is not altogether unrealistic is supported by the fact that the share of health is already almost 10 per cent of GDP in several European countries and 14 ½ per cent in the United States. Figure 18: Social expenditures as a percentage of GDP, 1995-96 to 2020-21
Social security and welfare expendituresSocial security and welfare expenditures largely comprise transfer payments to beneficiaries. They presently represent just under 10 per cent of GDP. Age and service pensions and family payments comprise the bulk of social security outlays and these are principally borne by the Commonwealth Government. Transfers to the unemployed and disabled persons are of lesser magnitude but nevertheless are quite sizeable, about $6-7 billion in each case. Reduced entitlements following as a result of 1996 Budget measures largely account for lower social security expenditures over the forward estimates period. These expenditures are projected to fall to 8.7 per cent of GDP by 2000-01. Under the base case scenario it can be seen that the ageing of the population is projected to lead to social security expenditures increasing by ½ a percentage point to the Year 2021 as shown by Figure 18 above. The ageing population and growth in age and service pensions is anticipated to more than offset relatively lower family payments. We used projections by the Retirement Income Modelling Task Force (RIM, 1997), which show that age pension outlays are expected to rise by 0.7 percentage points to 3.7 per cent of GDP to the Year 2021. The RIM projections make allowance for factors such as the Superannuation Guarantee, changes to preservation arrangements and changes in labour force participation and so on. These factors all influence earnings, superannuation and therefore age pension entitlements over the longer term. Labour and employment expendituresThe Commonwealth Government announced a fundamentally different approach to the delivery of labour market services in the 1996 Budget that resulted in major changes to labour market programmes. As a result, public labour market and employment expenditures are projected to almost halve in size as a share of GDP over the forward estimates period. Beyond the forward estimates labour and employment expenditures are projected to decline marginally as a share of GDP from 0.38 per cent in 2000-01 to 0.35 per cent to the Year 2021. As shown by Figure 18, labour and employment expenditures account for a relatively small share of social expenditures. Total social expendituresThe 1996 Budget measures are expected to lead to a sizeable reduction in all areas of social expenditure and in combination, as a share of GDP, they are expected to fall from 24.6 per cent in 1995-96 to 22.9 per cent by 2000-01. Interestingly, social expenditures as a share of GDP are projected to regain 1995-96 levels by the Year 2016. Then by the end of the projection period social expenditures are projected to increase to 25.7 per cent of GDP by the Year 2021. Thus in one sense the 1996 Budget measures negate the impact of the ageing population for the next twenty years. The key factors driving longer term movements in social expenditures are the ageing population, and its impact on health and age pension expenditures, and also rapidly increasing health care costs. As Figure 18 shows, the health care sector represents the major source of expansion in social expenditures over the longer term. Finally, increasing health and welfare costs are projected to more than fully offset the impact of diminishing education and labour market expenditures beyond the forward estimates period. [1] Australian Institute of Health and Welfare, Health Expenditure Bulletin, No. 12, December 1996. Part VConclusionThis paper has endeavoured to identify some key factors that are likely to impact on education funding into the future. It is evident that population growth and changes in the age structure represent a steady, though moderate pressure, on education funding over the longer term. It is more difficult to predict the influence of broader factors such as longer term trends in participation and the impact of social expenditures. Historical experience shows that changes in participation have been a major source of pressure on education funding in the past. Thus it seems worth repeating that future trends in mature age participation and developments in lifelong learning could have significant ramifications for education funding over the next twenty years, as could a move by young people to undertake longer courses. This paper has argued that changes in education funding must be seen in the broader context of likely trends in social expenditures. It would appear the ageing population and rapidly escalating health care costs represent real sources of upward pressure on health and welfare expenditures and to this extent are likely to act as a constraint on growth of education expenditures over the longer term. In the final analysis, expenditures on education will reflect not only the impact of these broader forces but also their interplay with decisions by government and community concerning the appropriate level of resources to be allocated to education. Technical Appendix : Sources and Methods - Projections of Social ExpendituresThis technical appendix documents the sources and methods used to develop projections of social expenditures, that is, projections of education, health, welfare and labour market expenditures to the year 2020-21. A major part of this exercise involves examining the likely impact of changing demographic patterns on education, health, welfare and labour market expenditures. In addition, we also incorporate the impact of changes in educational participation and trends in rising health costs in developing projections of social expenditures. Essentially the methodology assumes that the distribution of social expenditures across demographic groups is constant over time (based on the existing distribution in some base period) and that changes in the demographic composition of the population, given a constant distribution of social expenditures across demographic groups, dictates changes in the pattern of social expenditures changes over time. The inputs for developing projections of social expenditures using the methodology described above are three fold. First, we need estimates of per capita social expenditures across demographic groups at the given base period. Second, we need a set of population projections to measure potential changes in demographic patterns. Third, we need projections of GDP and some assumption about whether social expenditures increase in line with living standards to be able to sensibly demonstrate relative pressures on social expenditures arising from the ageing of the population. There are some broader comments about the methodology that need to be made at the outset before the remainder of the technical appendix turns to a description of the three inputs listed above. A major objective of the exercise is to explore the pressures on social expenditures from the ageing population. An additional point of focus is to identify pressures on government outlays. For this reason, we are not only interested in movements in consumption and capital expenditures, but also movements in benefit payments and grants. Benefits and grants tend to be recorded in government accounts according to their source of origin, for example, Youth Allowance payments are recorded as social security and welfare payments. However, in the ABS’s National Accounts, Youth Allowance payments are recorded on the expenditure side of the accounts as consumption expenditures on food, clothing, housing and the like. Therefore, in the analysis that follows, we have included benefit payments and grants so that we can observe pressures on government education, health, welfare and labour market expenditures. We define social expenditures as those expenditures that are likely to have a disproportionate impact on different age groups. For example, educational participation is generally concentrated among younger age groups whereas older age groups record higher per capita health expenditures. If we take something like defence expenditures or general economic outlays, it is difficult to imagine that these types of expenditures have a disproportionate impact on different age groups. Therefore for the purposes of this paper, we define social expenditures as public and private education and health expenditures and, public social security and welfare and labour market expenditures. Information on Commonwealth, State and Local government outlays for the relevant sectors was derived from ABS, Government Finance Statistics, 5512.0 to ensure outlays by sector were derived on a consistent basis. (There was one exception to this rule in that data on Commonwealth higher education outlays were derived from administrative sources owing to problems with the treatment of payments from the Higher Education Contributions Scheme (HECS) Trust Fund in the ABS’s public accounts. See below for a more detailed explanation of the treatment of HECS payments.) Information on private expenditures in the education and health sectors was derived from ABS, Expenditure on Education, 5510.0, 1995-96 and the Australian Institute of Health and Welfare’s, Health Expenditure Bulletin, No. 13, July 1997, respectively. The ABS, Government Finance Statistics, 5512.0, provides information on government outlays by end purpose for education, health, welfare and labour market outlays. Therefore we can calculate total government expenditures in each of these sectors. However, it should be noted at the outset that we are not able to accurately attribute expenditures by source, that is, by Commonwealth or State and Local government. Where Commonwealth government transfers to State and Local governments are designated for a specific purpose, for example in primary and secondary education, we attribute these to the Commonwealth government in the relevant sector. We also deduct these transfers from State and Local government expenditures in the relevant sector to avoid the problem of double counting. However, the Commonwealth government also provides general purpose grants to the States and Local governments. These payments are not included in Commonwealth government expenditures shown in this paper (they are recorded as other purpose payments, whereas only payments for education, health, welfare and labour market purposes are presented in this paper). However, they do appear in State and Local government expenditures in the ABS data to the extent that they are recorded as outlays by end purpose. That is, State and Local governments may choose to spend some part of general purpose payments from the Commonwealth government on schools, hospitals and the like. Therefore, while the problem of double counting is avoided, ideally these expenditures should be attributed to the Commonwealth government but are sourced as the State and Local government in this paper. That is, while the overall level of education, health, social security and welfare, and labour market expenditures is correctly identified, their distribution between the different levels of government can only be regarded as an approximation. In general, this is likely to have the effect of underestimating Commonwealth government expenditures and overestimating State and Local government expenditures. 1995-96 was used as the base year for our analysis. While the base year estimates for government outlays were derived from ABS, Government Finance Statistics, 5512.0, 1996-97, we use information gathered from forward estimates in the Commonwealth Budget papers for the period 1996-97 to 2000-01 to form projections of Commonwealth expenditures in the short to medium term. This is to ensure our projections of growth in Commonwealth expenditures are consistent with those in the forward estimates. We deflate the forward estimates by CPI forecasts (from Budget papers) to derive projections of real Commonwealth expenditures. That is, all expenditures are measured in 1995-96 base year prices. Appendix I: Per capita social expenditures by demographic groupEducationSchools Commonwealth government expenditures Total Commonwealth outlays on schools in 1995-96 were derived from ABS, Government Finance Statistics, 5512.0, 1996-97, Table 13. It was assumed Schools outlays comprised outlays on Primary and Secondary education outlays plus Other education outlays. Projections of Commonwealth schools, vocational and other education, and higher education outlays were based on growth rates contained in the forward estimates for the relevant sectors from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 for the period 1995-96 to 2000-01. The forward estimates include Student Assistance and General Administration outlays. These were distributed across schools, vocational and other education, and higher education sectors as follows. For the purposes of distributing Student Assistance outlays we obtained forward estimates of Austudy expenditures classified by tertiary and secondary sectors from unpublished departmental administrative records. We assumed all primary Austudy expenditures were allocated to the schools sector and tertiary Austudy expenditures were allocated to the higher education and vocational and other education sectors according to the distribution of Austudy recipients attending higher education and vocational and other education over the year to December 1996 where the latter included Austudy recipients in the other tertiary category. Data on the distribution of Austudy recipients were derived from unpublished departmental administrative records. We assumed Student Assistance outlays were distributed across the three sectors of education in the same proportion as the distribution of Austudy expenditures across sectors. General administration outlays were distributed pro-rata across the schools, vocational and other education, and higher education sectors as shown by the forward estimates (following the distribution of Student Assistance outlays across the sectors). This enabled us to derive, from the forward estimates, a set of growth rates for Commonwealth outlays in the schools, vocational and other education, and higher education sectors. These growth rates were then applied to the ABS estimates of Commonwealth outlays in the base year, 1995-96 (from Government Finance Statistics, 5512.0, 1996-97, as noted above), for the respective sectors. In order to derive projections of Commonwealth expenditures on schools beyond the forward estimates period from 2001-02 to 2020-21, we need to combine per capita school expenditures by demographic group with population projections. For the schools sector it was assumed that per capita school expenditures by demographic group were distributed according to the age distribution of full-time school students, ABS, Schools, Australia, 4221.0, 1995. One drawback of this approach is that it does not actually record expenditures by age group. There is an implicit assumption that it costs the same, for example, to educate each primary and secondary student. We then combined per capita school expenditures by demographic group with the relevant population projections (see later for source) for the period 2001-02 to 2020-21 to derive projections of Commonwealth expenditures on schools. State and Local government expenditures The base figure for state and local government expenditures on schools in 1995-96 was derived from total State, Territory and Local government outlays on primary and secondary education plus other education from Table 29, ABS, Government Finance Statistics, 5512.0, 1995-96 less Commonwealth grants to the states for primary and secondary education plus other education for current and capital outlays, Tables 15 and 16, Government Finance Statistics, 5512.0, 1995-96. The base figure for State and Local government expenditure on schools was then distributed across demographic groups according to the age distribution of full-time school students, ABS, Schools, Australia, 4221.0, 1995, to derive per capita State and Local government expenditure on schools. These were combined with the relevant population projections to derive projections of State and Local government expenditures on schools for the period 1996-97 to 2020-21. Private expenditures The base figure for private expenditures on schools was derived from unpublished data from Table 1, ABS, Expenditure on Education, 5510.0, for private final consumption expenditure in primary/secondary education and pre-school education in 1995-96. Per capita private expenditures on schools were derived in similar fashion to per capita Commonwealth, State and Local government expenditures on schools. The base figure for private expenditures on schools was distributed across demographic groups according to the age distribution of full-time school students. Per capita private expenditures on schools were then combined with population projections to derive projections of private expenditures on schools for the period 1996-97 to 2020-21. Scenarios The base case scenario for projections of school expenditures assumed no change in school retention rates and that expenditures increase in line with population projections and living standards (GDP per capita). The base year for the projection is 1995-96. The school retention rate in 1995 was 72.2 per cent ABS, Schools, Australia, 4221.0. As an alternative scenario it was assumed the Year 12 apparent retention rate in all States increased in line with the highest apparent retention rate in Queensland of 77.9 per cent, relative to the base case assumption of 72.2 per cent. The alternative scenario was based on the latest available data when the projections were prepared (1997), when the national apparent retention rate was 71.8 per cent. In 1999, the national retention rate was 72.3 per cent. Vocational education Commonwealth government expenditures Total Commonwealth government expenditures on vocational education in 1995-96 were derived from Table 4, ABS, Expenditure on Education, 5510.0, 1995-96, from Commonwealth government outlays on technical and further education. It was assumed this base year figure moved forward over the period 1996-97 to 2000-01 in line with forward estimates for vocational and other education (including some part of Commonwealth government Austudy and general administration expenditures allocated to vocational and other education as discussed above). Commonwealth government expenditures on vocational education in 2000-01 were distributed across demographic groups according to the age distribution of clients in vocational programs at 30 June 1996 from Table 1, Australian Vocational Education and Training Statistics : In detail, 1996, National Centre for Vocational Education Research. Per capita Commonwealth government expenditures on vocational education for demographic groups were then combined with population projections to derive projections of Commonwealth government expenditures on vocational and other education for the period 2001-02 to 2020-21. State and Local government expenditures The base figure for State and Local government expenditures on vocational education in 1995-96 was derived from State, Territory and Local government outlays on technical and further education from Table 29, ABS, Government Finance Statistics, 5512.0, 1995-96 less Commonwealth grants to the States, Territories and Local governments for tertiary education, Tables 15 and 16, Government Finance Statistics, 5512.0, 1995-96. Grants to the States, Territories and Local government were distributed between the vocational and higher education sectors on a pro-rata basis according to the distribution of intergovernmental grants between university and technical and further education in Table 3, ABS, Expenditure on Education, 5510.0, 1995-96. Projections of State and Local government expenditures on vocational education for the period 1996-97 to 2020-21 were derived in a similar manner to State and Local government expenditures on schools. That is, from the base year figure deriving per capita expenditures for demographic groups, based on the age distribution of students in vocational programs, and then combining this information with population projections. Private expenditures The base figure for private expenditures on vocational education was derived from unpublished data from Table 1, ABS, Expenditure on Education, 5510.0, for private final consumption expenditure in the post-secondary and tertiary education sectors in 1995-96. Private final consumption expenditures in tertiary education were distributed across the vocational and higher education sectors pro-rata according to the distribution of total government outlays in technical and further education and the balance of tertiary education respectively in Table 3, ABS, Expenditure on Education, 5510.0, 1995-96. Projections of private expenditures on vocational education for the period 1996-97 to 2020-21 were derived in a similar manner to state and local government expenditures on vocational education. That is, from the base year figure deriving per capita expenditures for demographic groups, based on the age distribution of students in vocational programs, and then combining this information with population projections. Scenarios The base case scenario for projections of vocational education expenditures assumed no change in vocational education participation rates and that expenditures increase in line with population projections and living standards (GDP per capita). As an alternative scenario it was assumed mature age participation rates, 25 years and over, doubled. Since per capita expenditures were based on the age distribution of students in vocational education, a doubling of mature age participation was modelled by simply doubling per capita expenditures on vocational education for those aged 25 years and over. Higher education Commonwealth government expenditures The treatment of Commonwealth government expenditures on higher education differed from that followed for the schools and vocational education sectors. Prior to the 1996-97 edition of ABS, Expenditure on Education, 5510.0, net advances to persons for Higher Education Contributions Scheme (HECS) purposes were included in government outlays (from the 1996-97 edition, net HECS advances were excluded from government outlays but shown separately as a financing item) and gross advances were included in private final consumption expenditure. To avoid the potential problem of double counting, the broad approach taken here was to obtain administrative data on government payments to higher education institutions (comprising grants to institutions plus payments to institutions from the HECS trust fund) and then make allowance for HECS payments made by individuals. Data and forward estimates on total payments to higher educations institutions (from the Commonwealth government) were obtained from administrative records for the period 1995-96 to 2000-01. These estimates included payments to institutions for HECS liabilities incorporating three elements; HECS liabilities paid to institutions, HECS upfront receipts paid to institutions and the HECS discount on upfront payments paid to institutions. Data on total Commonwealth payments were deflated by projections of CPI from Commonwealth budget papers to derive estimates of real outlays in 1995-96 prices. The task then was to subtract some portion of total Commonwealth payments to higher education institutions, reflecting the contribution paid by private individuals, and apportion this to private expenditures on higher education. Administrative estimates indicate that the HECS share of total Commonwealth payments to higher education institutions is projected to increase from 23 per cent in 1995/96 to 37.5 per cent by 2006-07 reflecting the changes to HECS arrangements announced in the 1996-97 Commonwealth Budget. However, some fraction of HECS payments are still likely to be met by the Commonwealth government rather than by individuals reflecting the discount on upfront payments and also that some indvidiuals will default on their HECS payments. Forward estimates contain estimates of the discount on upfront payments. We estimate that part of payments from the HECS trust fund likely to be met by the Commonwealth government as a result of non-payment of HECS debt by individuals. The Australian Government Actuary publishes a report on the provision for doubtful debt (Australian Government Actuary, Higher Education Contributions Scheme (HECS), Report on Doubtful Debt Provision, various editions). Essentially the increment in the stock of doubtful debt in any one year is assumed to represent the payment (flow) made by the Commonwealth government arising from non-payment of HECS debt. We examined historical trends in estimates of doubtful debt with a view to forming projections of the likely contribution to be made by the Commonwealth government in future years for non-payment of HECS debt. We assumed that contributions from individuals in the form of HECS payments would rise from 18.7 per cent of total payments to higher education institutions (combining grants and payments from the HECS trust fund) in 1995-96 to 30 per cent by 2006-07 and remain constant thereafter. Essentially the discount on upfront payments is assumed to be relatively stable, amounting to 2 per cent of total payments to higher education institutions and that Commonwealth payments in lieu of non-payment of HECS debt increase from just over 3 per cent in 1995-96 to around 5 per cent by 2006-07 and remain at that level thereafter. Thus from 2006-07 onwards, we assume 70 per cent of total payments to higher education institutions are paid by the Commonwealth government and 30 per cent by private individuals. Having established estimates of the Commonwealth government/private contribution to payments to higher education institutions we then include some part of Commonwealth government Austudy and general administration expenditures allocated to higher education as discussed above to derive forward estimates of Commonwealth government expenditures on higher education to the year 2000-01. Expenditures in the year 2000-01 were then distributed across demographic groups according to the age distribution of higher education students in 1995 (Selected Higher Education Student Statistics, 1995, unpublished data) to derive a set of per capita higher education expenditures for demographic groups. Per capita Commonwealth government expenditures on higher education for demographic groups were then combined with population projections to derive projections of Commonwealth government expenditures on higher education for the period 2001-02 to 2020-21. (Population projections are assumed to drive the demand for higher education and therefore total higher education expenditures. This in turn influences total payments to higher education institutions and our earlier assumptions about the distribution of these payments between the Commonwealth government and private individuals are superimposed on top of the broader trend in higher education expenditures.) State and Local government expenditures The base figure for State and Local government expenditures on higher education in 1995-96 was derived from state, territory and local government outlays on university education from Table 29, ABS, Government Finance Statistics, 5512.0, 1995-96 less Commonwealth grants to the States, Territories and Local governments for tertiary education, Tables 15 and 16, Government Finance Statistics, 5512.0, 1995-96. Grants to the States, Territories and Local government were distributed between the vocational and higher education sectors on a pro-rata basis according to the distribution of intergovernmental grants between university and technical and further education in Table 3, ABS Expenditure on Education, 5510.0, 1995-96. Projections of State and Local government expenditures on higher education for the period 1996-97 to 2020-21 were derived in a similar manner to State and Local government expenditures on schools. That is, from the base year figure deriving per capita expenditures for demographic groups, based on the age distribution of students in higher education, and then combining this information with population projections. Private expenditures There are essentially two components to projections of private expenditures on higher education: HECS expenditures and non-HECS private expenditures on fees, student services and the like. So far as projections of private HECS expenditures are concerned, there are several factors that are used as input to the development of the projections, as noted above in the discussion relating to Commonwealth government higher education expenditures:
So far as non-HECS private expenditures are concerned, once again there are several factors that are used as input to the development of the projections. The base figure for non-HECS private expenditures on higher education was derived from unpublished data from Table 1, ABS, Expenditure on Education, 5510.0, for private final consumption expenditure in the tertiary education sector in 1995-96. Private final consumption expenditures in tertiary education were distributed across the vocational and higher education sectors pro-rata according to the distribution of total government outlays in technical and further education and the balance of tertiary education respectively in Table 3, ABS, Expenditure on Education, 5510.0, 1995-96. Projections of non-HECS private expenditures for the period 1996-97 to 2020-21 were derived in a similar manner to State and Local government expenditures on higher education. That is, from the base year figure deriving per capita expenditures for demographic groups, based on the age distribution of students in higher education, and then combining this information with population projections. There is one further factor that contributes to the projections of non-HECS private expenditures. As part of the reforms to higher education announced in the 1996-97 Budget, institutions were permitted to introduce fee-paying opportunities in undergraduate courses, equivalent to 25 per cent of total enrolments in any course. This policy change was modelled as an addition to payments to higher education institutions (grants plus HECS payments), over and above those driven by the forward estimates and population growth. For the purposes of preparing the projections, a conservative assumption was adopted that undergraduate fee-paying opportunities would increase from 1 per cent of total payments to higher education institutions in 1997-98 and remain at 5 per cent from 2001-02 onwards. Scenarios The base case scenario for projections of higher education expenditures assumed no change in higher education participation rates and that expenditures increase in line with population projections and living standards (GDP per capita). As an alternative scenario it was assumed mature age participation rates, 22 years and over, doubled. Since per capita expenditures were based on the age distribution of students in higher education, a doubling of mature age participation was modelled by simply doubling per capita expenditures on higher education for those aged 22 years and over.HealthProjections of government health expenditures were developed for hospitals, nursing homes, pharmaceuticals and, the medical services and benefits sectors. Projections of private expenditures were developed by assuming that the ratio of private to total government health expenditures in each sector in the base period was held constant over the projection period. Hospitals Commonwealth government expenditures Total Commonwealth outlays on hospitals in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for category 2513 Other Admitted Patients. Projections of Commonwealth outlays on hospitals were based on growth rates contained in the forward estimates for Hospital Services from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 for the period 1995-96 to 2000-01. In order to derive projections of Commonwealth expenditures on hospitals beyond the forward estimates period from 2001-02 to 2020-21, we need to combine per capita hospital expenditures by demographic group with population projections. For each sector of health it was assumed that per capita expenditures by demographic group were distributed according to the age distribution of health patients in each of the sectors. One drawback with this approach is that it does not actually record expenditures by age group. There is an implicit assumption that it costs the same, for example, to treat younger and older patients. For the hospitals sector, it was assumed per capita hospital expenditures were distributed according to the age distribution of hospital days over a two week period as shown in, ABS, National Health Survey, 4364.0, 1995. We then combined per capita hospital expenditures by demographic group with the relevant population projections (see later for source) for the period 2001-02 to 2020-21 to derive projections of Commonwealth expenditures on hospitals. State and Local government expenditures Total State and Local government outlays on hospitals in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for category 2513 Other Admitted Patients ) less Commonwealth grants to the states for hospitals and other institutional services for current and capital outlays, Tables 15 and 16, Government Finance Statistics, 5512.0, 1995-96. The base figure for State and Local government expenditure on hospitals was then distributed across demographic groups according to the age distribution of hospital patient days over a two week period, ABS, Naional Health Survey, 4364.0, 1995, to derive per capita State and Local government expenditure on hospitals. These were combined with the relevant population projections to derive projections of State and Local government expenditures on hospitals for the period 1996-97 to 2020-21. Private expenditures Private expenditures on hospitals were derived from the Australian Institute of Health and Welfare’s (AIHW), Health Expenditure Bulletin, No. 13, July 1997, but with some adjustments to ensure estimates of government expenditures from AIHW were consistent with those from ABS, Government Finance Statistics, 5512.0, 1996-97. First, we take non-government (private) health expenditures figures by sector for 1994-95 from Table 17, Health Expenditure Bulletin, July 1997 (medical benefits and services is derived as a residual item with a deduction for tax expenditures so that the figure for total non-government expenditures in 1994-95 is consistent with the corresponding figure presented in Table 5). These figures are adjusted pro-rata so that they add to total non-government (private) expenditures for 1995-96 from Table 5, Health Expenditure Bulletin, July 1997. Second, there is a further adjustment to ensure that the non-government/government health expenditure figures from Table 5, Health Expenditure Bulletin, July 1997 are consistent with ABS government health expenditures in 1995-96. Then as for State and Local government expenditure on hospitals, the base figure for private expenditure on hospitals was then distributed across demographic groups according to the age distribution of hospital patient days over a two week period, ABS, National Health Survey, 4663.0, 1995, to derive per capita private expenditure on hospitals. These were combined with the relevant population projections to derive projections of private expenditures on hospitals for the period 1996-97 to 2020-21. Nursing homes Commonwealth government expenditures Total Commonwealth outlays on nursing homes in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for category 2530 Nursing Homes for the Aged. Projections of Commonwealth outlays on nursing homes were based on growth rates contained in the forward estimates for Nursing Home Subsidies and Domiciliary Care Services from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 for the period 1995-96 to 2000-01. In order to derive projections of Commonwealth expenditures on nursing homes beyond the forward estimates period from 2001-02 to 2020-21, we need to combine per capita nursing home expenditures by demographic group with population projections. For the nursing homes sector, it was assumed per capita nursing homes expenditures were distributed according to the age distribution of new entrants to nursing homes. We obtained administrative data from the Department of Health and Family Services (DHFS) on the age distribution of new entrants to nursing homes for the fortnight ending 13 June 1997. It is worth noting the flow of new entrants is not the same as the stock of nursing home residents. Nevertheless, advice from DHFS is that the age distribution of the flow of new entrants is likely to be reasonably representative of the age distribution of all nursing home residents given that the average duration of stay in nursing homes is of relatively short duration. We then combined per capita nursing home expenditures by demographic group with the relevant population projections (see later for source) for the period 2001-02 to 2020-21 to derive projections of Commonwealth expenditures on nursing homes. State and Local government expenditures Total State and Local government outlays on nursing homes in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for category 2530 Nursing Homes for the Aged. It was assumed there were no Commonwealth grants to the States for nursing home expenditures. The base figure for State and Local government expenditure on nursing homes was then distributed across demographic groups according to the age distribution of the flow of new entrants to nursing homes as described above to derive per capita State and Local government expenditure on nursing homes. These were combined with the relevant population projections to derive projections of State and Local government expenditures on nursing homes for the period 1996-97 to 2020-21. Private expenditures Private expenditures on nursing homes in the base period 1995-96 were derived in similar fashion to private expenditures on hospitals as described above. Then as for State and Local government expenditure on nursing homes, the base figure for private expenditure on nursing homes was then distributed across demographic groups according to the age distribution of new entrants to nursing homes to derive per capita private expenditure on nursing homes. These were combined with the relevant population projections to derive projections of private expenditures on nursing homes for the period 1996-97 to 2020-21. Pharmaceuticals Commonwealth government expenditures Total Commonwealth outlays on pharmaceuticals in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for category 2560 Pharmaceutical medical aids and appliances. Projections of Commonwealth outlays on pharmaceuticals were based on growth rates contained in the forward estimates for Pharmaceutical Services and Benefits from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 for the period 1995-96 to 2000-01. In order to derive projections of Commonwealth expenditures on pharmaceuticals beyond the forward estimates period from 2001-02 to 2020-21, we need to combine per capita pharmaceutical expenditures by demographic group with population projections. For the pharmaceuticals sector, it was assumed per capita pharmaceutical expenditures were distributed according to the age distribution of persons taking prescribed medicines over a two week period as shown in, ABS, National Health Survey, 4663.0, 1995. We then combined per capita pharmaceutical expenditures by demographic group with the relevant population projections (see later for source) for the period 2001-02 to 2020-21 to derive projections of Commonwealth expenditures on hospitals. State and Local government expenditures Total State and Local government outlays on pharmaceuticals in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for category 2560 Pharmaceutical medical aids and appliances. It was assumed there were no Commonwealth grants to the States for pharmaceuticals. The base figure for State and Local government expenditure on pharmaceuticals was then distributed across demographic groups according to the age distribution of persons taking prescribed medicines over a two week period, ABS, National Health Survey, 4663.0, 1995, to derive per capita State and Local government expenditure on pharmaceuticals. These were combined with the relevant population projections to derive projections of State and Local government expenditures on pharmaceuticals for the period 1996-97 to 2020-21. Private expenditures Private expenditures on pharmaceuticals in the base period 1995-96 were derived in similar fashion to private expenditures on hospitals as described above. Then as for State and Local government expenditure on pharmaceuticals, the base figure for private expenditure on pharmaceuticals was then distributed across demographic groups according to the age distribution of persons taking prescribed medicines over a two week period, ABS, National Health Survey, 4663.0, 1995, to derive per capita private expenditure on pharmaceuticals. These were combined with the relevant population projections to derive projections of private expenditures on pharmaceuticals for the period 1996-97 to 2020-21. Medical benefits and services Commonwealth government expenditures Total Commonwealth outlays on medical benefits and services in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 in residual fashion as outlays on all health services less outlays on hospitals, nursing homes and pharmaceuticals. We include other health outlays, such as community health and health research outlays, with medical benefits outlays (instead of with hospitals, nursing homes or pharmaceuticals outlays) because we assume these are distributed across age groups in similar fashion. Projections of Commonwealth outlays on medical benefits and services were based on growth rates contained in the forward estimates for Medical Services and Benefits, ATSI Health outlays, Other Health Services and General Administration outlays from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 for the period 1995-96 to 2000-01. In order to derive projections of Commonwealth expenditures on medical benefits and services beyond the forward estimates period from 2001-02 to 2020-21, we need to combine per capita medical benefits and services expenditures by demographic group with population projections. For the medical benefits and services sector, it was assumed per capita medical benefits and services expenditures were distributed according to the number of doctors’ consultations by age group over a two week period as shown in, ABS, National Health Survey, 4663.0, 1995. We then combined per capita medical benefits and services expenditures by demographic group with the relevant population projections (see later for source) for the period 2001-02 to 2020-21 to derive projections of Commonwealth expenditures on hospitals. State and Local government expenditures Total State and Local government outlays on medical benefits and services in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for all health outlays other than hospitals, nursing homes and pharmaceuticals less Commonwealth grants to the states for other health outlays for current and capital outlays, Tables 15 and 16, Government Finance Statistics, 5512.0, 1995-96. The base figure for State and Local government expenditure on medical benefits and services was then distributed across demographic groups according to the number of doctors’ consultations by age group over a two week period, ABS, Naional Health Survey, 4663.0, 1995, to derive per capita State and Local government expenditure on medical benefits and services. These were combined with the relevant population projections to derive projections of State and Local government expenditures on medical benefits and services for the period 1996-97 to 2020-21. Private expenditures Private expenditures on medical benefits and services in the base period 1995-96 were derived in similar fashion to private expenditures on hospitals as described above. Then as for State and Local government expenditure on medical benefits and services, the base figure for private expenditure on medical benefits and services was then distributed across demographic groups according to the number of doctors’ consultations by age group over a two week period, ABS, National Health Survey, 4663.0, 1995, to derive per capita private expenditure on medical benefits and services. These were combined with the relevant population projections to derive projections of private expenditures on medical benefits and services for the period 1996-97 to 2020-21.Social security and welfareProjections of social security and welfare expenditures were prepared for the Commonwealth government and State and Local governments sectors and we do not include any private social security and welfare expenditures. Commonwealth government expenditures are further broken down by sector but State and Local government expenditures are a one line item since they are relatively small scale in nature in comparison with Commonwealth government expenditures. Commonwealth government expenditures Total Commonwealth outlays on social security and welfare in 1995-96 were derived from ABS, Government Finance Statistics, 5512.0, Table 17. Commonwealth outlays on social security and welfare by sector in 1995-96 as shown in the Commonwealth Budget Paper No. 1, 1996-97 were adjusted pro-rata to add to the ABS figure. Outlays on Other Welfare Programmes, Aboriginal Advancement Programmes nec and Recoveries and Repayments were combined with Assistance to Families and Other Welfare Programmes since the age distribution of these outlays was assumed to be closest to that of the latter. General administration outlays were then distributed pro-rata across the remaining sectors. Age and service pensions Using the estimate of age and service pension expenditures in 1995-96 derived above, we then applied growth rates contained in the forward estimates for the period 1995-96 to 2000-01 for age and service pensions from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 to derive age and service pension outlays for the period 1996-97 to 2000-01. Beyond the forward estimates period, we used projections of age and service pensions as a percentage of GDP supplied by the Retirement Income Modelling (RIM) taskforce. We adjusted the nominal age and service pensions figure derived from RIM projections for 2000-01 (see Section III later for a description of assumptions relating to GDP and prices) so that it equated to our derived estimate of age and service pensions expenditure in 2000-01. We then applied growth rates implied by the RIM projections to derive projections of nominal age and service pensions for the period 2001-02 to 2020-21. Assistance to veterans and dependants, excluding service pensions We exclude assistance to veterans and dependants expenditures (excluding service pensions) from the analysis. This primarily comprises expenditures on veterans disability pensions. Whereas veterans service pensions can be treated in some senses as a substitute for the age pension, the treatment of veterans disability pensions is unclear. It might be argued that as the present cohort of veterans diminishes over time, that projections of veterans disability pensions expenditures should decline over time. However, it may be the case that at some time in the future, there may be new claimants to veterans disability pensions. Excluding this category of expenditure may have the effect of showing stronger growth in social expenditures by way of comparison with another scenario which included these expenditures but where they declined over time. Assistance to people with disabilities Using the estimate of assistance to people with disabilities in 1995-96 derived above, we then applied growth rates contained in the forward estimates for the period 1995-96 to 2000-01 for assistance to people with disabilities from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 to derive expenditures on assistance to people with disabilities for the period 1996-97 to 2000-01. Commonwealth government expenditures on assistance to people with disabilities in 2000-01 were distributed across demographic groups according to the age distribution of Disability Support Pensioners. Data on the latter were derived from Table 6, Disability Support Pension Customers, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. Per capita Commonwealth government expenditures on assistance to people with disabilities by demographic group were then combined with population projections to derive projections of Commonwealth government expenditures on assistance to people with disabilities for the period 2001-02 to 2020-21. Assistance to families and other welfare programmes Using the estimate of assistance to families and other welfare programmes in 1995-96 derived above, we then applied growth rates contained in the forward estimates for the period 1995-96 to 2000-01 for assistance to families and other welfare programmes from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 to derive expenditures on assistance to families and other welfare programmes for the period 1996-97 to 2000-01. Commonwealth government expenditures on assistance to families and other welfare programmes in 2000-01 were distributed across demographic groups in the following manner. This category covers a wide range of family and welfare payments. Unfortunately we only have information relating to the age distribution of a limited number of these payments. In the current exercise we utilised information on the age distribution of sole parent pensioners, Widows Class B pensioners and persons receiving greater than minimum family payment. We assume all outlays relating to parenting payment (categories of expenditure within the assistance to families and other welfare programmes sector were separately identified for this exercise) are distributed across age groups according to the age distribution of sole parent pensioners. This information was derived from Table 39, Sole Parent Pension Customers, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. We assume outlays relating to Other Assistance to Widows were distributed across age groups according to the age distribution of persons receiving the Widows Pension Class B. This information was derived from Table 50, Widow Pension Class B Customers, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. All other outlays in this category were assumed to be distributed across age groups according to the age distribution of persons receiving greater than minimum family payment. It was assumed these other outlays most closely resembled family payments in terms of their benefits to age groups. Information on the age distribution of greater than minimum family payments was derived from Table 34, Greater Than Minimum Family Payment Customers : Characteristics by Family Type, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. Per capita Commonwealth government expenditures in each of these categories were then combined to derive per capita expenditures on assistance to families and other welfare programmes by demographic group in 2000-01. This latter information was then combined with population projections to derive projections of Commonwealth government expenditures on assistance to families and other welfare programmes for the period 2001-02 to 2020-21. Assistance to the unemployed and sick Using the estimate of assistance to the unemployed and sick in 1995-96 derived above, we then applied growth rates contained in the forward estimates for the period 1995-96 to 2000-01 for assistance to the unemployed and sick from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 to derive expenditures on assistance to the unemployed and sick for the period 1996-97 to 2000-01. Commonwealth government expenditures on assistance to the unemployed and sick in 2000-01 were distributed across demographic groups in the following manner. Newstart Allowance outlays (categories of expenditure within the assistance to unemployed and sick sector were separately identified for this exercise) were distributed across age groups according to the age distribution of Newstart Allowance Customers, Job Search Allowance Customers, Youth Training Allowance Customers and Mature Age Allowance Customers (the latter are all 60-64 year-old males). Information on the age distribution of these customers was derived from Tables 23, 25, 26 and 27, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. Sickness Allowance outlays were distributed across age groups according to the age distribution of Sickness Allowance Customers. Information on the age distribution of these customers was derived from Table 16, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. Partner Allowance outlays were distributed across age groups according to the age distribution of Partner Allowance Customers. Information on the age distribution of these customers was derived from Table 29, June 1996, DSS Customers : A Statistical Overview, Department of Social Security, 1996. Per capita Commonwealth government expenditures in each of these categories were then combined to derive per capita expenditures on assistance to the unemployed and sick by demographic group in 2000-01. This latter information was then combined with population projections to derive projections of Commonwealth government expenditures on assistance to the unemployed and sick for the period 2001-02 to 2020-21. State and Local government expenditures Total State and Local government outlays on social security and welfare in 1995-96 were derived from ABS, Government Finance Statistics, 5512.0, 1995-96, Table 29 less Commonwealth grants to the states for social security and welfare for current and capital outlays, Tables 15 and 16, Government Finance Statistics, 5512.0, 1995-96. The base figure for State and Local government expenditure on social security and welfare was then distributed across demographic groups according to the distribution of Commonwealth expenditures on assistance to families and other welfare programmes described above, to derive per capita State and Local government expenditure on social security and welfare. These were combined with the relevant population projections to derive projections of State and Local government expenditures on social security and welfare for the period 1996-97 to 2020-21. Labour market and employmentProjections of labour market and employment expenditures were prepared for the Commonwealth government and State and Local government sectors and we do not include any private labour market and employment expenditures. Commonwealth government expenditures Total Commonwealth outlays on labour market and employment in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1996-97 for categories, 3331 Vocational Training and 3339 Other Labour and Employment Affairs. Commonwealth outlays on labour market and employment affairs for the Vocational and Industry Training, Assistance to Jobseekers and Industry, and Employment Services sectors in 1995-96, as shown in the Commonwealth Budget Paper No. 1, 1996-97, were adjusted pro-rata to add to the ABS figure. We then applied growth rates contained in the forward estimates for the period 1995-96 to 2000-01 for Vocational and Industry Training, Assistance to Jobseekers and Industry, and Employment Services (from 1 July 1997, the latter outlays were recorded instead as social security and welfare outlays following the creation of the Commonwealth Service Delivery Agency) from Commonwealth Budget Paper No. 1, 1996-97 and 1997-98 to derive expenditures on these categories for the period 1996-97 to 2000-01. We derived per capita expenditures by demographic group for vocational and industry training expenditures and labour market assistance to jobseekers and industry according to the distribution of programme expenditures within each of these categories and the age distribution of participants within those programmes. In the vocational and industry training category, expenditures comprise outlays on the traineeship and apprenticeship schemes. Data on expenditures were derived from the Department of Employment, Education, Training and Youth Affairs, Annual Report, 1995-96. Data on the age distribution of participants in these programmes was derived from unpublished administrative data from the Department. A similar approach was used to derive per capita expenditures by demographic group for the category of labour market assistance to jobseekers and industry. Expenditure in the following programmes in 1995-96 was derived from the Department of Employment, Education, Training and Youth Affairs, Annual Report,1995-96: Jobskills, New Work Opportunities, Jobstart, Landcare and Environment Action Programme (LEAP), Jobtrain, Special Intervention Programme (SIP), Jobclubs, Skillshare, Mobility Assistance Scheme and the New Enterprise Incentive Scheme (NEIS). Similarly, data on the age distribution of participants in these programmes was derived from unpublished administrative data from the Department. This information enabled us to derive per capita Commonwealth government labour market and employment expenditures by demographic group for 2000-01. This was combined with population projections to derive projections of Commonwealth government expenditures on labour market and employment for the period 2001-02 to 2020-21. State and Local government expenditures Total State and Local government outlays on labour market and employment in 1995-96 were derived from unpublished data from ABS, Government Finance Statistics, 5512.0, 1995-96, for the category 3339 Other labour and employment affairs. (It was assumed there were no Commonwealth grants to the States for labour market and employment and that grants in the broader Other Economic Affairs category were allocated to the residual category 3390, Other Economic Affairs nec). The base figure for State and Local government expenditure on labour market and employment was then distributed across demographic groups according to the age distribution of participants in the Jobstart programme. It was assumed the age distribution of State and Local government expenditures in this area more closely resembled the age distribution of expenditures in the Jobstart programme. From this information we derived per capita State and Local government expenditure on labour market and employment by demographic group. These were combined with the relevant population projections to derive projections of State and Local government expenditures on labour market and employment for the period 1996-97 to 2020-21.Appendix II: Population ProjectionsThe population projections used in the paper were derived from ABS, Projections of the Population Projections, Australia, States and Territories, 1995-2051, 3222.0, 1996. the projections were based on the Series A projections using the Fertility II and Overseas Migration I assumptions. That is, the total fertility rate was assumed to decline from 1.85 in 1994 to 1.75 by the Year 2004 and remain constant thereafter. Net migration was assumed to decline from 120,000 in 1995-96 and fall to 70,000 by 1998-99 and remain constant in absolute terms thereafter.Appendix III: GDP, living standards, productivity and pricesThe projections were prepared using 1995-96 prices as the basis for preparing projections of real expenditures. An estimate of Gross Domestic Product (GDP) for the income measure in 1995-96 in current prices was obtained from ABS, National Income Expenditure and Product, Australian National Accounts, 5206.0. Estimates of growth in real GDP (A) for the period 1996-97 to 2000-01 was derived from Table 4, Commonwealth Budget Paper No. 1, Statement 2 Economic and Fiscal Outlook, 1997-98. Estimates of growth in nominal GDP for the period 2001-02 to 2020-21 were obtained from the Retirement Income Modelling (RIM) Taskforce. These estimates were based on an assumption that growth in the Consumer Price Index (CPI) was 2.5 per cent per annum over this period and this figure was used to derive growth in real GDP. RIM estimates of growth in nominal GDP were based on a set of consistent assumptions about growth in prices, labour productivity, average hours worked, unemployment, labour force participation and population. RIM estimates were based on assumptions of growth in labour productivity of 1.25 per cent per annum over the longer term and that the unemployment rate fell to 6 per cent by 2002. (Note that RIM have subsequently revised their assumption about growth in labour productivity to 1.5 per cent per annum since projections were supplied for the purposes of this paper.) Our base case scenario assumes that education, social security and welfare and labour market and employment expenditures increased in line with living standards or real GDP per capita. However, a different assumption was used for health expenditures given that historically over the period 1982-83 to 1994-95 real per capita growth in health expenditures has been of the order of 2.8 per cent per annum. However, once allowance for ageing of the population is made and the impact this has on growth in per capita health expenditures, it was considered appropriate to assume per capita health expenditures increased by 2 per cent per annum for all demographic groups over the projection period.BibliographyAndrews, L., (1997), The Effect of HECS on Interest in Undertaking Higher Education, Higher Education Division, Department of Employment, Training and Youth Affairs, AGPS, Canberra. Australian Bureau of Statistics, Projections of the Populations of Australia, States and Territories, 1995-2051, 3222.0, 1996, AGPS, Canberra. Australian Bureau of Statistics, Estimated Resident Population by Sex and Age, States and Territories of Australia, 3201.0, various issues, AGPS, Canberra. Australian Bureau of Statistics, Expenditure on Education, 5510.0, 1995-96, AGPS, Canberra. Australian Bureau of Statistics, Government Finance Statistics, 5512.0.0, various issues, AGPS, Canberra. Australian Bureau of Statistics, National Health Survey, 4364.0, 1995, AGPS, Canberra. Australian Bureau of Statistics, National Income Expenditure and Product, Australian National Accounts, 5206.0, various issues, AGPS, Canberra. Australian Bureau of Statistics, Schools, 4221.0, various issues, AGPS, Canberra. Australian Bureau of Statistics, Transition From Education to Work, unpublished data. Australian Institute of Health and Welfare, Health Expenditure Bulletin No. 13, July 1997, AGPS, Canberra. Australian Government Actuary, Higher Education Contributions Scheme (HECS), Report on Doubtful Debt Provision, various editions. Department of Employment, Education, Training and Youth Affairs, Annual Report, 1995-96, AGPS, Canberra. Department of Employment, Education, Training and Youth Affairs, Selected Higher Education Statistics Students, unpublished data. Department of Social Security, DSS Customers : A Statistical Overview, 1996, AGPS, Canberra. Department of Treasury, Commonwealth Budget Paper No. 1, 1996-97 and 1997-98, AGPS, Canberra. Economic Planning and Advisory Council, An Ageing Society, Background Paper No. 37, 1994, AGPS, Canberra. Karmel, Tom (1999), Financing Higher Education in Australia, Occasional Paper Series 99-D, Higher Education Division, Department of Education, Training and Youth Affairs. National Commission of Audit, Report to the Commonwealth Government, 1996, AGPS, Canberra. National Centre for Vocational Education Research, Australian Vocational Education and Training, Statistics : In detail , 1996. National Centre for Vocational Education Research, Australian Vocational Education and Training, Statistics, unpublished data. Organisation for Economic Co-operation and Development, Policies for Higher Education : Conference on Future Structures of Post-Secondary Education, ‘Problems in the Transition from Elite to Mass Higher Education’ by Martin Trow, 1974. Review of Higher Education Financing and Policy 1998, Learning for Life : Final Report, AGPS, Canberra. Retirement Income Modelling Task Force (RIM), 1997, unpublished projections provided for this paper. Urban, M., Jones, E., Smith, G., Evans, C., MacLachlan, M. and Karmel, T. (1999), Completions : Undergraduate academic outcomes for 1992 commencing students, Occasional Paper Series 99-G, Higher Education Division, Department of Education, Training and Youth Affairs, AGPS, Canberra. |
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