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SUMMARY OF FINDINGS
As in 1993-94, these categories together accounted for around half of household expenditure on goods and services. The next highest category was recreation, with average weekly household expenditure of $89 per week, representing 13% of the total.
AVERAGE WEEKLY HOUSEHOLD EXPENDITURE ON GOODS AND SERVICES
DIFFERENCES BETWEEN HOUSEHOLDS
The level and pattern of expenditure differs between households, reflecting characteristics such as income, household composition, household size and location.
In 1998-99, households in the lowest income quintile (the lowest 20% of households when ranked according to income) spent on average $343 per week on goods and services, compared with $1,171 by households in the highest income quintile.
Household income also affects the composition of a household's weekly expenditure. For example, food and non-alcoholic drinks accounted for 20% of the expenditure on goods and services of households in the lowest income quintile, compared with 17% for households in the highest income quintile. In general, the proportion spent on housing, household services, and domestic fuel and power also declined as household income rose, while the proportion spent on transport, recreation, clothing and footwear, and alcohol, increased.
PROPORTION OF EXPENDITURE ALLOCATED TO GOODS AND SERVICES BY THE LOWEST AND HIGHEST INCOME QUINTILE GROUPS
Social and demographic characteristics
The level and composition of household income and expenditure is highly related to the social and demographic characteristics of household members.
For example, households in the lowest income quintile were more likely to be lone person households and to rely on government pensions and allowances as their principal source of income. Similarly, households in the highest income quintile were more likely to be couple, one family households and to have employee income as their principal source of income.
Households in which the level of weekly expenditure on goods and services was significantly below the average of $699 for all households included:
In contrast, households in which the level of expenditure on goods and services was significantly above the average included:
Household characteristics can also help to explain the variation in the composition of expenditure. For example, housing costs accounted for only 7% of total expenditure on goods and services of those households who owned their home outright; at the other extreme, households renting from other than a state or territory housing authority spent 23% of their total expenditure on housing.
The level of expenditure varied between the six states. Households in New South Wales recorded the highest average weekly expenditure at $740, followed by those in Victoria at $718. The lowest expenditure was in Tasmania ($593 per week).
Expenditure also varied between capital cities and rural areas. The average weekly expenditure of households located in capital cities was $747, compared with $616 in rural areas. Capital city households spent considerably more on housing, food, clothing and footwear, and recreation than those in rural areas. The capital city with the highest average weekly household expenditure was Darwin at $906 per week, followed by Canberra at $860.
CHANGES SINCE 1993–94
The overall increase in average weekly household expenditure on goods and services between 1993-94 and 1998-99 was $97 or 16%. Over the same period the price of goods and services, as measured by the CPI, rose by 10%.
The broad expenditure categories recording the largest increases in average weekly expenditure were:
At the other extreme, average weekly household expenditure on clothing and footwear declined by $2 or 5%. Clothing prices, as measured by the CPI, did not change between 1993-94 and 1998-99. Clothing was the only CPI group to record no upward price movement over this period.
INCREASE IN AVERAGE WEEKLY HOUSEHOLD EXPENDITURE ON GOODS AND SERVICES, 1993–94 – 1998–99
1 This publication presents summary results from the 1998-99 Household Expenditure Survey (HES). The survey collected detailed information about the expenditure, income and household characteristics of households resident in private dwellings throughout Australia.
2 The statistics presented in this publication are intended to present a broad overview of data items collected during the 1998-99 HES. Emphasis has been given to highlighting the differing household expenditure patterns and levels revealed when average weekly household expenditure is cross-classified by various household characteristics (e.g. income levels and sources, geographic location and family composition of the household) and reference person characteristics.
3 The 1998-99 Household Expenditure Survey, Australia: User Guide (cat. no. 6527.0), expected to be released in September 2000, will assist users in evaluating and interpreting results from this survey.
4 The 1998-99 HES is similar to the 1993-94 survey. The main differences are:
5 The 1998-99 HES commenced field enumeration throughout Australia in July 1998. Field enumeration was completed in June 1999. Further information concerning the 1998-99 survey and the five earlier surveys conducted in 1974-75, 1975-76, 1984, 1988-89 and 1993-94 can be obtained from the 1998-99 User Guide (6527.0).
CONCEPTS AND DEFINITIONS
6 The concepts and definitions of income, expenditure and households in the HES are described in the following section. Refer to the Glossary for the definitions of other HES terms.
7 The household is the basic unit of analysis in the HES. It is defined as a group of people who usually live in the same dwelling and make common provision for living essentials.
8 Households therefore have the following characteristics:
9 The household is adopted as the basic unit of analysis because it is assumed that sharing of the use of goods and services occurs at this level. If smaller units, say persons, are adopted, then it is difficult to know how to attribute to individual household members the use of shared items such as food, accommodation and household goods.
11 Estimates of selected other payments (income tax, mortgage repayments (selected dwelling) and superannuation and life insurance) are also provided.
12 Estimates of average weekly expenditure do not refer to a given week. Average weekly expenditure was calculated by dividing expenditure by the number of weeks in the recall period or reporting period over which it was collected.
13 Expenditure was classified according to the Household Expenditure Classification. A copy of the classification is included in the 1998-99 User Guide (6527.0).
14 Although the HES is primarily a survey of household expenditure, household income estimates are produced to help explain variations in expenditure levels and patterns and to identify groups of special interest (e.g. households with low incomes).
15 HES estimates of income refer to:
16 Receipts which were excluded from income because they were not cash, regular or recurring consisted of the following:
-inheritances and legacies;
-non-recurring gifts from other households;
-capital repayment of loans from other households;
-maturity payments received on life insurance policies; and
-lump sum compensation for injuries.
-loans and credit obtained.
-non-monetary gifts from other households.
See description of expenditure in-kind, above, for inclusions.
17 Estimates of weekly income do not refer to a given week. Income was collected using a number of different reporting periods, such as the last financial year for own business and property income and last pay for wages and salaries and other sources of private income. Reported income was divided by the number of weeks in the reporting period to obtain weekly income.
18 Income was collected according to source. Main sources of income include employee income, own business income, government pensions and allowances and other income (including property income such as rent, interest and dividends and other transfer income such as regular recurring receipts from superannuation and child support).
DIFFERENCE BETWEEN INCOME AND EXPENDITURE
19 The HES provides information about both the income and the expenditure of households, but it would be misleading to regard the difference between average weekly income and the sum of the items of average weekly expenditure as shown in the tables in this publication as a measure of saving.
20 First, to be properly understood, the concept of household saving needs to be articulated along with the concept of household wealth (assets less liabilities), and all forms of income and expenditure need to be measured and classified consistently with these concepts. The HES does not attempt to do this. For example, it does not provide information about capital gains or windfall gains such as inheritances. Rather, it focuses on the regular and recurring forms of income; expenditure on current consumption of goods and services; the major component of regular current transfers (income tax); and two major items of expenditure which can be regarded as investment ('mortgage repayments - principal (selected dwelling)' and 'superannuation and life insurance'). The two items of investment expenditure are included in the HES because they are a significant regular commitment of many households which have to be financed from regular income.
21 Second, there are significant timing differences between the different components of income and expenditure collected:
22 The timing problem is likely to be greatest for households for which the major source of income is unincorporated business activity. Recorded income will relate to the previous financial year, while expenditure will mostly relate to a period within the current financial year. If business profitability is significantly different between the two years, then there may be a significant discrepancy between the recorded income and expenditure components which do not reflect the saving pattern of the household. While such differences will disappear to a certain extent through summing across households, there may still be an impact on aggregate estimates if, for example, all farmers had a bad season in one year and a good season in the following year. More importantly, there will be a definite impact on the quintile analysis of HES data.
23 HES income and expenditure estimates therefore do not balance for individual households or for groups of households and the difference between income and expenditure cannot be considered to be a measure of saving.
SCOPE AND COVERAGE
24 Only residents of private dwellings in Australia were in scope. Private dwellings were houses, flats, home units, caravans, garages, tents and other structures that were used as places of residence at the time of interview. These were distinct from special dwellings which included hotels, boarding houses and institutions. Residents of special dwellings were excluded because of differences in their lifestyle and accommodation. Also excluded were households containing foreign defence force staff, foreign diplomats or diplomatic staff.
25 Information was collected from usual residents of private dwellings in all areas of Australia except remote and sparsely settled areas, where:
26 Information for each household was collected using:
27 Sample copies of the above documents are available for purchase.
SURVEY DESIGN AND ESTIMATION
28 The sample was designed to produce reliable estimates for households resident in private dwellings aggregated for Australia, for each state and for the capital cities in each state and territory. Of the selected dwellings, there were 8,908 in the scope of the survey, of which 6,893 (77%) were included as part of the final estimates.
29 Of the households selected in the sample, there were 2,015 which did not contribute to the values of HES expenditure or income. Such households included those who could not be contacted, had language problems, refused to participate, or were affected by death or illness of a household member. Also excluded were those in which the reference person or spouse did not respond to key questions in the survey such as income.
30 Of the households which provided most of the required HES information but were unable, or unwilling, to provide all of it, some were able to be retained in the sample and their missing values deduced or imputed.
31 For some of these households, missing information could be deduced using additional information supplied on the questionnaire (such as prices for given quantities and types of bread and milk purchased from given types of outlets).
32 In the remainder of cases, the missing information was imputed. Imputation is the process of replacing missing values with substitute values during processing. Imputation was carried out at two levels:
33 In either case, the record providing the missing information is known as the donor record. Donors were selected so that, as far as possible, the information they provided would be an appropriate proxy for the information that was missing. Depending on which values were being imputed, donors were taken from the pool of complete households or individual records with complete information for the block of questions in which the missing information was located.
34 To better match donors to recipient records, both sets of records were ordered according to characteristics (such as number of adults and children present) associated with the blocks of variables being imputed. For example, recipients with missing information were matched with donors who fell into the same classes as themselves. The classes were fairly broad so that sufficient numbers of donors could be found in similar classes to the recipients.
35 The sample on which estimates were based, or the final HES sample, is composed of households for which all necessary information is available. The information may have been wholly provided at the interview or may have been completed through imputation for partially responding households. The 1998-99 HES final sample includes approximately 600 households which had at least one imputed value. Over 40% of these households, in fact, had only a single value missing.
HES FINAL SAMPLE: NUMBER OF HOUSEHOLDS, 1998-99
36 Expansion factors, or weights, are values by which information for sample households is multiplied to produce estimates for the whole population.
37 Initial weights, based on the sample design, were equal to the inverse of the probability of selection. Weights for each member of the household were the same as the weight for the household itself.
38 In previous surveys, these initial weights have been adjusted to account for non-response. For the 1998-99 HES the demographic and geographic information available for non-respondents was analysed to determine whether a strong relationship existed between household non-response and its demographic and geographic characteristics. No strong relationship was detected so no adjustment to the initial weights to account for non-response was required.
39 To adjust for under-enumeration and to align survey estimates with independent population estimates, the weights were calibrated against person and household benchmarks. Using an iterative procedure, the weights were adjusted so that person and household estimates conformed with external person and household benchmarks. The two person benchmarks which were used are: state/territory population estimates by seven age categories (including 65 years and over); and labour force status estimates (from Labour Force Survey data) by capital city / balance of state or territory by sex by five age categories. The two household benchmarks were: nine categories of household composition (including lone person households) by capital city / balance of state or territory; and state by capital city / balance of state or territory.
40 The household benchmarks were based on provisional estimates of numbers of households in Australia. The benchmarks were adjusted to include households and persons residing in private dwellings only and therefore do not, and are not intended to, match estimates of the total Australian resident population published in other ABS publications.
41 The benchmarks do not include some 175,000 people living in sparsely settled areas. The exclusion of these people will have only a minor impact on any aggregate estimates that are produced for individual states and territories, with the exception of the Northern Territory where such people account for over 20% of the population. HES estimates for the Northern Territory other than Darwin are not considered reliable, and so only Darwin estimates are shown separately in this publication.
42 Estimates produced from the survey are usually in the form of averages (e.g. average weekly household expenditure on clothing and footwear), or counts (e.g. total number of households who own their dwelling). For counts, the estimate was obtained by summing the weights of the responding households in the required group (e.g. those households owning their dwelling). Averages were obtained by adding the weighted household values, and then dividing by the estimated number of households. For example, average weekly expenditure on clothing and footwear by Victorian households is the weighted sum of the average weekly expenditure of each selected household in Victoria who reported such expenditure, divided by the estimated number of households in Victoria. Note that the denominator is the total number of households and not just the number of households which have reported expenditure on the particular item.
43 Income tax payments were estimated for all households using taxation criteria for 1998-99 and the income and other characteristics of household members reported in the survey.
RELIABILITY OF ESTIMATES
44 The estimates provided in this publication are subject to two types of error.
45 Non-sampling error can occur whether the estimates are derived from a sample or from a complete collection. Three major sources of non-sampling error are:
46 Non-sampling errors are difficult to measure in any collection. However, every effort was made to minimise these errors. In particular, the effect of the reporting and processing errors described above was minimised by careful questionnaire design, intensive training and supervision of interviewers, asking respondents to refer to records whenever possible and by extensive editing and quality control checking at all stages of data collection and processing.
47 The error due to non-response was minimised by:
48 The HES estimates are based on a sample of possible observations. Hence, they are subject to sampling variability and estimates may differ from the figures that would have been produced if information had been collected for all households.
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