6523.0 - Household Income and Income Distribution, Australia, 2011-12  
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 19/12/2013   
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These fact sheets provide a broad overview of the key concepts and data sources for measuring household economic wellbeing. The Household Economic Wellbeing fact sheet series currently comprises:

The series may be expanded in the future to cover other aspects of these important statistics.


When considering the circumstances of households, the key economic wellbeing factors that affect people's material standard of living are income, consumption and wealth.

Income can be used to support consumption of goods and services, such as food, clothing, housing and leisure activities. Alternatively, it can be saved and invested to increase wealth which can be used at a later date to support consumption.

Some people with low incomes have considerable wealth allowing them to maintain consumption levels above their current income. People with low reserves of wealth may face financial difficulty in times of need, such as during any period of reduced income or substantial unexpected expenses.

Diagram 1 illustrates this relationship, although people's actual wellbeing is affected by individual circumstances and lifestyle choices.

Diagram 1. Components of economic wellbeing

    Box 1. Key resources for statistics on household income, consumption and wealth

    Canberra Group Handbook on Household Income Statistics, Second Edition, 2011
    Reflects new international standards for household income statistics and provides guidance on conceptual and practical issues related to their production and use.
    Available at <http://www.unece.org/statshome/publications-amp-resources/publications.html>

    OECD Guidelines for Micro Statistics on Household Wealth
    Provides an internationally agreed set of standard concepts, definitions and classifications for micro wealth statistics and best practice for compiling and analysing wealth statistics.
    Available at <http://www.oecd.org/statistics/guidelines-for-micro-statistics-on-household-wealth.htm>

    OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth
    Presents an internationally agreed framework to support the joint analysis of micro-level statistics on household income, consumption and wealth as three separate but interrelated dimensions of people’s economic wellbeing.
    Available at <http://www.oecd.org/statistics/icw-framework.htm>
Income and wealth accumulation over the life cycle

Income levels and wealth vary over a person's life and are affected by two main factors, age and labour force participation. Incomes tend to grow until middle age. Wealth tends to be gradually accumulated during the working lives of household members and used during retirement. (Graphs 1 and 2)

Graph Image for Graph 1. Gross household income by age of reference person, 2011-12
Graph Image for Graph 2. Net worth by age of reference person, 2011-12

Key concepts for measuring economic wellbeing

The definitions used to measure the economic wellbeing of people can have a significant impact on the results. The Australian Bureau of Statistics (ABS) follows international best practice in producing micro statistics relating to household economic resources.


The most comprehensive measure of income is compiled from the ABS Survey of Income and Housing (SIH) and the ABS Household Expenditure Survey (HES). This definition aligns with new international standards released in 2004 and fully adopted from SIH 2007–08 and HES 2009–10:
    Income consists of all current receipts, whether monetary or in kind, that are received by the household or by individual members of the household, and which are available for, or intended to support, current consumption.

The first international wealth standards were published by the OECD in 2013.
    Wealth refers to economic resources in the form of assets and liabilities. Wealth, or net worth, is the value of all the assets (e.g. property, bank accounts and shares) owned by a household less the value of all its liabilities (mortgages and other loans) at a particular point in time. Net worth may be negative when a household's liabilities exceed its assets.

Consumption expenditure

The international definition of consumption expenditure is summarised as:
    Household consumption expenditure is the value of consumer goods and services acquired, used or paid for by a household through direct monetary purchases, own account production, barter or as income in kind.

In the HES, expenditure is valued as the cost of goods and services acquired during the reference period for private use, whether or not the goods were paid for or consumed in that period. Expenditure is net of refunds and trade-ins. Consumption expenditure includes in kind income from employers, such as subsidised housing or the use of a car for private purposes.

Broadening the income measure

In recent years the ABS has made significant progress in extending its measurement of household income to reflect real world changes and enhance analytical opportunities. This includes developing new measures to allow the full economic circumstances of different types of households to be compared. In particular, the ABS has produced:
    a) imputed rent (IR) estimates since 2003–04
    b) social transfers in kind (STIK) allocations from SIH 2011–12 (previously only based on HES data)
    c) final income estimates since 1984.
a) Imputed rent

What is it?

Income from imputed rent is allocated to owner occupiers and households living in subsidised private rentals e.g. renting from a family member. For owner occupiers, income from imputed rent is the estimated market rent of a dwelling less housing costs normally paid by a landlord such as mortgage interest, rates, insurance and repairs. For renters, it is the difference between market rent and actual rent paid.

Why include imputed rent in income?

Housing is one of the most significant living costs borne by many households. The inclusion of imputed rent in income provides a broader picture of the economic wellbeing of owner occupied and rent-subsidised households relative to other households, allowing more meaningful comparisons of the wellbeing of people living in different tenure types.b) Social transfers in kind

What are they?

Social transfers in kind are goods and services provided by governments that benefit individuals but are provided free or at subsidised prices. Examples include free or subsidised education, health and child care.

Why include STIK in income?

STIK have a significant impact on the wellbeing of people and on the measurement of the distribution of income. This is important for comparisons within and across countries. In Australia, many government services have been designed to assist those most in need of financial support. The allocation of benefits differs between households, reflecting characteristics such as household composition, life cycle stages, household size and income.

The inclusion of IR and STIK increased the mean equivalised disposable household income (EDHI) from $918 to $1220 per week in 2011–12 and reduced the inequality of income distribution across households. (Graph 3)

Graph 3. Distribution of equivalised disposable household income with and without IR and STIK, 2011-12
Mean EDHI increases with the inclusion of imputed rent and social transfers in kind.
Source: ABS Survey of Income and Housing (6523.0) Appendix 4 Social transfers in kind

c) Final incomeWhat is it?

Final income is equal to household private income plus social assistance benefits in cash (e.g. age and disability support pensions, Family Tax Benefit) and STIK less income taxes and taxes on production (e.g. GST and taxes on alcohol and cigarettes). Both household income and expenditure are required to estimate final income. This data is available whenever the HES is conducted, most recently in 2009–10. (Diagram 2)

Why is it important?

Final income shows the full effect of government expenditure and taxes on the distribution of income among private households in Australia. This allows policy makers to understand the effects of changes in either government revenues or spending that directly impact on the economic wellbeing of households.

The net effect of government benefits and taxes in 2009–10 was to increase average incomes of households in the three lowest quintiles and decrease those of the two highest quintiles. (Graph 4)

Graph Image for Graph 4. Private and final household income, by equivalised private household income quintile, 2009-10

Diagram 2 illustrates the relationship between the different income concepts presented in this Fact Sheet Series.

Diagram 2. Income concepts and components


Why is income and wealth distribution important?

Economic and social analysts and policy makers are interested in the distribution of resources and how this affects the wellbeing of society and individuals, particularly people's ability to acquire the goods and services required to satisfy their needs.

Questions that researchers ask include:
  • How unequal is the distribution of income and wealth? How does this compare with earlier years, or with other countries?
  • What are the characteristics of households considered most at risk of economic hardship? Which are in greatest need of financial support?
  • Do people have sufficient incomes and wealth accumulation in their working lives and to maintain an adequate standard of living in retirement?
Equivalence scales

Why is an equivalence scale used?

As household size increases, consumption needs also increase but there are economies of scale. An equivalence scale is used to adjust household incomes to take account of the economies that flow from sharing resources and enable more meaningful comparisons across different types of households. For a lone person household equivalised income is equal to actual income. For households comprising more than one person, it is the estimated income that a lone person household would need to enjoy the same standard of living as the household in question.

How are equivalising factors calculated?

Equivalising factors are calculated based on the size and composition of the household, recognising that children typically have fewer needs than adults. The ABS uses the OECD-modified equivalence scale which assigns a value of 1 to the household head, 0.5 to each additional person 15 years or older and 0.3 to each child under 15 years.

Table 1 shows that a couple household with one child would need $1,800 weekly disposable income to have the same equivalised disposable household income (EDHI) as a lone person household with a disposable income of $1,000.

Table 1. Examples of equivalised weekly disposable household income

Household composition
Equivalising factor (x)
Disposable income (y)
Equivalised disposable income (y/x)

Lone person
Couple only
(1 + 0.5) = 1.5
Couple with one child under 15 years
(1 + 0.5 + 0.3) = 1.8
Group household with three adults
(1 + 0.5 + 0.5) = 2.0

Relationship between equivalisation of income, consumption and wealth

Equivalence scales used for household income are equally applicable for consumption measures. There is less agreement about how to equivalise household wealth as wealth is often built up during a person's working life and then used during retirement when the composition of the household might be quite different. However, when wealth is being used to support current consumption, particularly for households at risk of economic hardship, household wealth should be equivalised with the same scale used to equivalise household income and consumption.

Analysis of households and persons

There are two common ways of presenting analysis of households:
  • number of households, or
  • number of people in households.
In the former, each household contributes the same regardless of its size, e.g. a four person household would have the same representation as a person living alone.

To provide a better understanding of the circumstances of people it is often preferable to study people in households e.g. the number of people in Australian households experiencing economic hardship. In this analysis, each person is attributed with the characteristics of the household to which they belong, e.g. household income is used to determine whether it is a low or high income household but analysis is about numbers of people experiencing hardship. This approach keeps the focus on individual circumstances while recognising that people share household resources.

Summary measures

There are several summary measures commonly used for analysing household economic wellbeing.


Counts provide an estimate of the total number of people or households with a particular characteristic and are derived by summing the survey weights of each observation of interest. In sample surveys the weights enable extrapolation of the survey responses to official population estimates.


The arithmetic mean, or average, is the sum of all income divided by the number of observations. Advantages of the mean are that it is easy to calculate and the means of all subcomponents sum to the mean of all observations. Its drawbacks are the effect of extreme values and asymmetry of the distribution, both of which are relevant for income and wealth data. For example, a small number of very wealthy and a large number of relatively poor households may have the same average income or wealth as a population where there is equal distribution of resources.


Medians are calculated by ranking all observations from the lowest to the highest. The middle observation of the distribution is the median. Compared to the mean, the median is a more stable measure and is less affected by extreme values and sample fluctuations. However, median values of subcomponents do not add up to the median of all observations.

Distribution measures

Measures of the distribution of income and wealth help to describe and understand how economic resources are shared across the population and households.

Frequency distribution

Frequency distributions show the proportion of people or households with a particular level of income or wealth. To produce the distribution, the item of interest is ranked by value and the population grouped into classes. The ABS currently uses $50 ranges for weekly income and $100,000 ranges for wealth.

It is useful to include the summary statistics such as the mean and median in the frequency distributions. Income and wealth distributions tend to be asymmetrical, with a small number of people having relatively high income or wealth and a much larger number having relatively low income or wealth. (Graph 1)Graph 1. Distribution of equivalised disposable household income, 2011-12
Graph 1 shows that the distribution of income is asymmetrical with a relatively large number of people having low income and a relatively small number having high income
Source: ABS data available on request, Survey of Income and Housing (6523.0)


Quantile is a term for groups formed by ranking the units of analysis (e.g. household or persons) in ascending order and calculating the shares of the total accruing to a given proportion of the units:
    • quintiles are formed when the population is divided into five equally sized groups
    • deciles into ten groups
    • percentiles into 100 groups.
Therefore the first quintile will comprise the first two deciles and the first 20 percentiles. The mean or the median may be used to summarise the circumstances within a quantile.

Graph Image for Graph 2. Equivalised disposable household income (EDHI), by quintile, 2011-12

Percentile ratios

The boundary between quantiles is usually expressed as the upper value of a particular percentile. The ABS publishes the upper value of each decile (P10 to P90). This provides the range of values in each quintile, e.g. the middle (3rd) quintile is formed by households with income/wealth between P40 and P60. The median of each quintile can also be determined, e.g. the median of the first quintile is P10, second quintile, P30, etc. The median of the whole population is P50.

Percentile ratios summarise the relative distance between two points on the income or wealth distribution. Percentile ratios will be less volatile than measures based on means, particularly at each end of the distribution. To illustrate the full spread of the income distribution, the percentile ratio should use points near the extremes e.g. the P90/P10 ratio. The P80/P20 ratio better illustrates the magnitude of the range for the majority of the population. The P90/P50 and P10/P50 ratios compare the ends of the distribution with the median and these are commonly used to understand how the wealthier compare to average and the poorer to average.

Table 2 shows that income is more equally distributed than wealth. In 2011–12, the equivalised income of households at the top of the 80th percentile (or fourth quintile) was 2.6 times higher than that of households at the top of the 20th percentile (or lowest quintile), whereas wealth was 10 times higher (P80/P20). Table 2. Ratio of values at top of selected percentiles, 2011–12

Equivalised disposable household income per week
Equivalised household net worth


Source: ABS Survey of Income and Housing (6554.0)

Shares of income or wealth

Income or wealth shares can be calculated and compared for each quantile of a population. The aggregate income/wealth of units in each quantile is divided by the total aggregate of the entire population to derive quantile share.

Graph 3 shows income and wealth shares by decile. Household wealth is more unequally distributed than household income. People in the three lowest equivalised income deciles received 13% of all income, whilst people in the three lowest equivalised wealth deciles held only 3% of all wealth in 2011–12.

Graph Image for Graph 3. Share of equivalised household income and net worth (a), 2011-12

Footnote(s): (a) Decile boundaries are derived separately for equivalised disposable income and net worth

Source(s): ABS Survey of Income and Housing (6554.0)

Gini coefficient

The Gini coefficient is a single statistical indicator of the degree of inequality. It equals zero when all people have the same level of income and equals one when one person receives all the income. In general the smaller the Gini coefficient, the more equal the distribution of income or wealth. Any increase in the income of a person with income greater than the median will always lead to an increase in the Gini coefficient, while an increase in the income of a person with income lower than the median will always lead to a decrease in the coefficient.

The distribution of income becomes more equal when imputed rent and social transfers in kind (STIK) are included in the income measure, down from 0.320 to 0.226 in 2011–12. (Table 3)

Table 3. Gini coefficient, by household income, 2011–12

Gini coefficient

Equivalised disposable income
Equivalised disposable income (incl. imputed rent)
Equivalised disposable income (incl. imputed rent and STIK)

Source: ABS Survey of Income and Housing, 2011–12 (6523.0) Appendix 4 Social transfers in kind

Measurement ErrorsSampling Error

Household survey estimates are based on a sample of possible observations and are subject to sampling variability. The sampling error is a measure of the variability that occurs by chance because a sample, rather than the entire population, is surveyed. One measure of the likely difference is given by the standard error (SE). Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate. The RSE is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer also to the size of the estimate.

The ABS annotates estimates with a RSE between 25% and less than 50% by a preceding asterisk (e.g. *3.4) to indicate they are subject to high SEs and should be used with caution. Estimates with RSEs of 50% or more are preceded with a double asterisk (e.g. **0.6), indicating that these estimates are considered unreliable for most purposes.

Significance Testing

To compare estimates between surveys or between populations within a survey it is important to determine whether apparent differences are 'real' differences between the corresponding population characteristics or simply the product of differences between the survey samples. A common approach is to determine whether the difference between the estimates is statistically significant by calculating the standard error of the difference between two estimates (x and y) and using that to calculate the test statistic using the formula below:

The absolute value of x minus y divided by the standard error times  x minus y

If the value of the statistic is greater than 1.96 there is good evidence of a statistically significant difference at 95% confidence levels between the two populations for the characteristic being tested. Otherwise, it cannot be stated with confidence that there is a real difference between the populations.


People living in low economic resource households are of particular policy and research interest because of their greater risk of experiencing economic hardship. This fact sheet summarises different methods available to identify these households and provides guidance on methods of analysing them.

There are many factors influencing whether people are experiencing economic hardship. The analysis of household economic wellbeing is enhanced significantly when the income, consumption and wealth dimensions are studied jointly, recognising they vary over the lifecycle:
  • income is affected by workforce participation
  • wealth tends to be accumulated during people’s working life and then consumed in retirement
  • younger people may have higher expenditure needs e.g. to buy a home or start a family.
In recognition of the importance of this, the ABS has collected both income and wealth in every Survey of Income and Housing (SIH) from 2003–04 (apart from 2007–08). The ABS Household Expenditure Survey (HES) has been conducted six yearly since 2003–04 on a subsample of SIH households. Expenditure, financial stress, income and wealth data are available for HES households. The ABS has also produced the Socio-Economic Indexes for Areas (SEIFA) since the 1986 Census. (Box 1)

    Box 1. Socio-Economic Indexes for Areas (SEIFA)

    Census data (including education, employment, occupation, income and housing) has been used by the ABS to identify the relative socio-economic advantage and disadvantage of geographic areas in Australia compared with other areas.

    The 2011 SEIFA includes an Index of Relative Socio-Economic Disadvantage (IRSD) and an Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD).

    As well as being used to analyse Census data, the IRSD and IRSAD by decile and/or quintile have also been added to survey files (including CURFs) for household surveys such as the SIH, HES and GSS from 2002 onwards.

    For more information: Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2011 (2033.0.55.001).

Composite measures of low economic resource households

Low economic resource measure

Diagram 1. Low economic resource households
Diagram 1. Low economic resource households

The ABS has developed a low economic resource measure (LER) that includes people who are simultaneously in the lowest four deciles of both equivalised disposable household income (EDHI) (including imputed rent) and equivalised household net worth (LER40). This measure therefore excludes people with either relatively high incomes or relatively high wealth. As a result it is more likely to correctly classify people at risk of experiencing economic hardship compared to measures using income or wealth alone.

The LER is a relative measure that classifies around 20% of people in low income, low wealth households. It does not identify whether these people are actually experiencing economic hardship. The actual proportion will vary over time as the joint distribution of income and wealth changes. One of the strengths of this measure is its ability to contrast the characteristics of the LER population with those in the low income and low wealth quintiles.

Table 1 compares selected characteristics of LER households to households with low income or low wealth only. The proportion of couple or lone person households where the reference person is 65 and over, reduces from 28% of low income households to 6% of LER households, reflecting their ability to draw on accumulated wealth. In contrast, whilst 35% of low income households are private renters, this group accounts for 52% of LER households.

Table 1. Persons in low economic resource households, 2011–12

Household characteristics
Low income (a)
Low wealth (b)
Low economic resource (LER40)(c)
All persons

Mean weekly household income
Equivalised disposable household income$
Equivalised disposable household income incl. imputed rent$
Mean equivalised net worth $'000
Tenure type
Owner without a mortgage%
Owner with a mortgage%
Private renter%
Select household groups
Couple family with dependent children%
One parent family with dependent children%
Couple or lone person, 65 and over%

* estimate has a relative standard error of 25% to 50% and should be used with caution
(a) Persons in the lowest two deciles of EDHI (incl. imputed rent)
(b) Persons in the lowest two deciles of equivalised household net worth
(c) Persons in the lowest four deciles of both EDHI (incl. imputed rent) and equivalised household net worth
Source: ABS Survey of Income and Housing (6523.0) Feature Article: Low Economic Resource Households

Other composite measures of economic hardshipThe LER measure can be broadened by considering experiences of 'financial stress' or 'missing out'. The indicators used to define these measures are listed in Table 3 of this fact sheet.

Graph 1 shows examples of LER measures by:
  • varying the cut-off for low income and low wealth 40th percentile (LER40) or 30th percentile (LER30), then
  • adding whether the household experienced 'financial stress' or 'missing out'.
In 2009–10, 23% of people lived in LER40 households and 15% in LER30 households. When experiences of ‘financial stress’ are also considered this reduces to 15% of LER40 and 11% of LER30 households.

Graph 1. Measures of low economic resource households, proportion of persons, 2009-10
Proportion of people in LER40 and LER30 households reduces when financial stress is also considered. LER40 (23% to 15%) and LER30 (15% to 11%)
Source: ABS data available of request, Household Expenditure Survey (6530.0)

Single dimension measurement of household economic wellbeing

When measuring economic wellbeing it is preferable to consider multiple dimensions, particularly income and wealth, however both measures are not always available. This section describes several commonly used single dimension measures of economic wellbeing.


Income is the most frequently available measure of economic wellbeing. For most households, it is the main resource used to meet daily expenses. However, analysis using income alone has significant limitations. Income can be volatile for people who are making transitions between study, jobs, into retirement or changing their hours of work e.g. to care for children. At these times, households may draw on other resources, such as using savings or increasing their debt.

Being able to identify households with accumulated wealth to supplement low incomes is desirable as these people are less likely to experience economic hardship than households without alternative resources to fall back on.

a) Relative poverty measures based on income

Many developed countries use relative poverty to measure the economic wellbeing of households. These measures identify the proportion of people with an income below a certain fraction of median EDHI. The OECD publishes various analyses based on poverty lines below 40%, 50% or 60% of median incomes (50% used most often), while Eurostat commonly uses 60% as the cut-off.

Limitations of relative poverty measures include:
  • the number of people in poverty is determined by an arbitrary fraction of income (which may not reflect actual hardship).
  • the proportion of people identified can change dramatically e.g. in Australia, real median incomes have risen significantly in recent years and the thresholds identified at 40% and 50% of the median are very sensitive to changes in single and couple pension payment points relative to the median.
  • the definition and measurement of income can have a significant impact e.g. imputed rent (IR) and social transfers in kind (STIK) are often excluded from income definitions. However, the benefits received from either owning a home or receiving subsided rent (valued by imputing an equivalent rental income), or from receiving services from the government, impact significantly on the economic wellbeing of particular groups e.g. a person able to access free or subsidised health care can be better off than a person with similar income but not able to access these social provisions.
Table 2 shows that the proportion of the Australian population below a relative poverty line varies between 20% (using 60% of median EDHI) and 2% (using 40% of median EDHI including IR and STIK).

Table 2. Relative poverty measures based on proportion below a percentage of median income, 2011–12

Equivalised disposable household income
Equivalised disposable household income incl. imputed rent
Equivalised disposable household income incl. imputed rent and STIK

40% of median income
50% of median income
60% of median income

Source: ABS data available on request, Survey of Income and Housing (6523.0)
b) Using the second and third deciles to describe low income households

While it is tempting to label all households in the lowest income decile as ‘low income’, ABS analysis suggests there are variable economic circumstances for households in this group. Households with nil or negative income, or income below government pension rates, make up almost one half of the lowest income decile. However, more than 40% of households in the lowest income decile have net worth in the top five wealth deciles, suggesting a temporary setback to their economic wellbeing, such as a temporary loss in their business operations or a temporary job loss.

Graph Image for Graph 2. Equivalised weekly income and expenditure, by equivalised income decile, 2009-10

Furthermore, people in the lowest decile of EDHI had average equivalised expenditure higher than those in the second income decile in 2009–10. (Graph 2) The ABS therefore uses the second and third income deciles to describe 'low income' households rather than the lowest income quintile.

However, as the lowest decile includes many households whose only source of income is a government pension or allowance, some people in the lowest income decile experience high levels of economic hardship. Therefore, for many analytical purposes a lower cut-off should be applied to only remove extreme low value households that may distort the results. In 2011–12, Age Pension rates (excluding supplementary payments) for singles and couples were around the 7th percentile of EDHI.

Financial stress indicators

While income and wealth statistics describe the economic resources available to people and expenditure statistics describe their consumption patterns, there are other issues relevant to understanding living standards e.g. a person with poor health and high health care costs may have reduced income for other purchases. In attempting to identify which households have the lowest economic wellbeing, other indicators of poor economic outcomes can be considered. Data relating to experiences of financial stress and missing out are collected in the HES. (Table 3)

Table 3. Indicators of financial stress in the last 12 months

    Financial stress experiences
    Missing out experiences

    Unable to raise $2000 in a week for something important

    Spend more money than received

    Could not pay gas, electricity or telephone bill on time

    Could not pay registration or insurance on time

    Pawned or sold something

    Went without meals

    Unable to heat home

    Sought assistance from welfare/community organisations

    Sought financial help from friends or family

    Could not afford holiday for at least one week a year

    Could not afford a night out once a fortnight

    Could not afford friends or family over for a meal once a month

    Could not afford special meal once a week

    Could only afford second hand clothes most of the time

    Could not afford leisure or hobby activities

Source: ABS Household Expenditure Survey, 2009–10 (6530.0)

Financial stress information can provide insight into people’s economic wellbeing although analysis needs to consider overall circumstances. Some individuals may have consumption priorities which differ from socially accepted norms of the 'basics of life'. In 2009–10, 20% of households in the highest EDHI quintile reported at least one financial stress indicator. (Graph 3)

Graph Image for Graph 3. Proportion of households experiencing financial stress in the last 12 months, by income quintile, 2009-10

Measuring persistent economic hardship

Another key policy interest is people experiencing long-term and persistent economic hardship as distinct from those experiencing short-term hardship.

Longitudinal datasets, such as the Household Income and Labour Dynamics Australia Survey (HILDA) and the ABS Australian Census Longitudinal Dataset (ACLD), are important sources for identifying people experiencing long-term economic hardship. The HILDA has been tracking the economic circumstances of many respondents since 2001. The ACLD will provide a five-yearly snapshot of the income and housing circumstances of people from 2006.

The SIH measures the short-term persistence of economic hardship by comparing income from the previous financial year with current year income. The circumstances of people with low incomes in both periods can be identified. Combined with wealth data which is more stable over time, this provides a more accurate picture of whether hardship is persistent.

As well as financial stress experiences, the HES also collects data on people's perception of their current financial circumstances compared to two years ago and their ability to save money.


Key data sources

There are many useful data sources providing information on the economic wellbeing among households in Australia. This fact sheet uses the ABS Data Quality Framework to provide information about the following key data sources (Tables 1 and 2):
  • Survey of Income and Housing (SIH)
  • Household Expenditure Survey (HES)
  • General Social Survey (GSS)
  • Census of Population and Housing (Census)
  • Australian Census Longitudinal Dataset (ACLD)
  • Australian System of National Accounts (ASNA)
  • Household Income and Labour Dynamics in Australia Survey (HILDA).
Administrative data can also be used to undertake analyses of wellbeing. Potential opportunities arising from some key datasets (Diagram 1):
  • Australian Taxation Office (ATO) Personal income tax
  • ATO Business income tax and Employer Pay as you go (PAYG) payment summaries
  • Centrelink and Department of Veterans’ Affairs (DVA) income support payments
  • Medicare Benefits Scheme (MBS).

Table 1. Key data sources

Data quality dimensionSurvey of Income and Housing (SIH)Household Expenditure Survey (HES)General Social Survey (GSS)

Institutional environment
Australian Bureau of Statistics

    Main economic wellbeing topics
Income, wealth, housing, labourExpenditure, income, wealth, financial stress, housing, labourIncome, financial stress, housing and homelessness, labour

    Other topics
Child careChild care, disabilitySocial inclusion topics e.g. social networks, feelings of safety, transport

    Data collection
Cross-sectional survey (CAPI) of all usual residents 15 years and overCross-sectional survey (CAPI) of all usual residents 15 years and overCross-sectional survey (CAPI) of one usual resident 18 years and over (15 years and over from 2014)

    Years available
1994–95 to 1997–98, 1999–2000, 2000–01, 2002–03, 2003–04 then every two years1984, 1988–89, 1993–94, 1998–99, 2003–04, 2009–102002, 2006, 2010

    Scope and populations included
Usual residents of private dwellings excl. very remote areas in Australia (97% of population)Usual residents of private dwellings excl. very remote areas in Australia (97% of population)Usual residents of private dwellings excl. very remote areas in Australia (97% of population)

    Period of field work
Financial year (June – July)Financial year (June – July)March – July

TimelinessBiennial survey, output released about 12 months after reference periodSix yearly survey, output released about 14 months after reference periodFour yearly survey, output released about 14 months after enumeration

    Sample size
About 14,600 householdsAbout 9,800 householdsAbout 15,100 households

    Response rates
About 80%About 73%About 88%

CoherenceMethodological and income definition improvements in 2003–04 and 2007–08Methodological and income definition improvements in 2003–04 and 2007–08Consistent methodology and definitions

DocumentationUser Guide (6553.0) User Guide (6503.0)User Guide (4159.0.55.002)
All User Guides including meta data and copy of questionnaire are available on ABS web site <www.abs.gov.au>, search by catalogue number

Summary outputsIncome incl. detailed tables (6523.0) Wealth publication (6554.0) Housing (4130.0)Expenditure incl. detailed state and commodity tables (6530.0 [2009–10] and 6535.0.55.001[2003–04]) Government benefits and taxes (6537.0)Publication (4159.0) State tables (4159.0.55.003 [2010] and 4159.[0…8].55.001 [2006])

MicrodataConfidentialised unit record files (CURFs) available – search 'Microdata' on ABS web site home page

Table 1. Key data sources continued

Data quality dimensionCensus of Population and Housing (Census)Australian Census Longitudinal Dataset (ACLD)Australian System of National Accounts (ASNA) Household, Income and Labour Dynamics in Australia Survey (HILDA)

Institutional environment
Australian Bureau of Statistics
Melbourne Institute (funded by Dept of Social Services)

    Main economic wellbeing topics
Income, housing, labour Income, housing, labour Income, consumption, savings and wealthIncome, expenditure and labour (core topics), wealth (on rotation)

    Other topics
Household and family composition, disability, language and cultureHousehold and family composition, disability, language and cultureNot applicableLife events and satisfaction, health, family, caring, attitudes, retirement plans

    Data collection
Census of all persons in Australia using self-enumerated paper or electronic questionnaireCensus of all persons in Australia using self-enumerated paper or electronic questionnaireVarious – business and household survey data; administrative dataLongitudinal survey (CAPI/CATI) of Australian residents 15 years and over

    Years available
Annually from 1911 to 1954, then 5 yearly from 1961 to 2011Census 2006 and 2011 linked dataAnnually and quarterly from July 1959 for income, consumption and savings; wealth from July 1988Wave 1 in 2001, then annually for Waves 2 to 12

    Scope and populations included
All residents of Australia and Australian territories (excl. Norfolk Island) (100% of population)All residents of Australia and Australian territories (excl. Norfolk Island) (100% of population)All households in Australia incl. non-private dwellings and non-profit institutions serving households Australian residents of private dwellings, excl. remote and very remote areas (96% of pop.) [Wave 1] from Wave 2 incl. remote and very remote

    Period of field work
Census night (in August)Census night (in August)Financial yearAbout 18 weeks during August to February

TimelinessFive yearly census, data released in waves (from 10 months after Census night)Available in late 2013Preliminary release after reference period – annual (4 months); quarterly (2 months) Annual survey, output released about 12 months after reference period

    Sample size
About 21.5 million peopleAbout 1 million peopleNot applicable9,500 households in Wave 11 (incl. 2,150 from Top-Up sample)

    Response rates
About 96%Not applicableNot applicableWave 1 response 66% Attrition rate each wave between 4% and 13%

CoherenceRegular classifications changes managed by producing correspondences between old and new classificationsRegular classifications changes managed by producing correspondences between old and new classificationsComplete time series recompiled to new basis whenever treatments changeConsistent methodology income model improved periodically

DocumentationHow Australia Takes a Census (2903.0) 2011 Census dictionary (2901.0) Census Products and Services (2011.0.55.001) How Australia Takes a Census (2903.0) 2011 Census dictionary (2901.0) Census Products and Services (2011.0.55.001)ASNA Concepts, Sources and Methods (5216.0)User manual, program library and questionnaires
All User Guides including meta data and copy of questionnaire are available on the ABS web site <www.abs.gov.au>, search by catalogue number

Summary outputsSelect 'Census' on ABS web site home page, then 'Data and analysis'
  • QuickStats
  • Community profiles
  • TableBuilder
  • DataPacks
  • Census Sample Files
  • ACLD
Select 'Census' on ABS web site home page, then 'Data and analysis'
  • QuickStats
  • Community profiles
  • TableBuilder
  • DataPacks
  • Census Sample Files
  • ACLD
  • Annual publication (5204.0)
  • quarterly publications (5206.0, 5232.0)
  • www.melbourneinstitute.com/hilda/biblio/

    MicrodataAccess via TablebuilderABS charged consultancies only via TablebuilderNot applicableCNEF and unit record data are available
    Table 2. Strengths and limitations of key data sources



  • Most accurate and representative measure of income and wealth distributions
  • Most comprehensive measures of income incl. imputed rent and social transfers in kind
  • Data collected over a 12 month period allows for seasonal variations
  • Wealth in every cycle since 2003–04 (excl. 2007–08)
  • Captures household consumption expenditure by very detailed commodity
  • Enables joint analysis of income, expenditure, wealth and financial stress
  • Fiscal incidence study undertaken for every HES
  • Coverage of total population
  • Output available for very fine level spatial analysis, e.g. statistical area
  • Analysis of special populations, e.g. migrants, disabled and Aboriginal and Torres Strait Islander peoples
  • Covers all economic activity of households
  • Provides per capita and savings estimates
  • Annual and quarterly time series available
  • Longitudinal analysis following circumstances of individuals over time
  • Survey conducted annually
  • Broad range of topics (core or on a rotational basis)
  • Unconfidentialised data available to approved Australian researchers (excl. name and address)

  • Limitations

  • Impact of income definition and methodological improvements on time series [mitigated by output of income for both current and previous income definitions (see Changes over time fact sheet 5)]
  • Income data affected by methodological improvements (as for SIH)
  • Some expenditure known to be under-reported e.g. alcohol and gambling
  • Personal income collected in ranges only
  • Method of calculating household income less accurate as based on ranged personal income data
  • Fully self-enumerated questionnaire may lead to higher levels of data misreporting
  • No household distributional data available across time
  • Household sector includes activity of non-profit institutions serving households
  • Longitudinal surveys prone to sample and attrition bias over time [Sample weight adjustments and wave 11 general sample Top-Up to mitigate bias]
  • More difficult to ensure sample represents total population than cross-sectional surveys

  • Subpopulations
    The economic circumstances of some subpopulations are of particular interest to researchers and policymakers as they have been identified as being at greater risk of experiencing economic disadvantage. As well as Census data, there are data sources specific to each subpopulation.


    The ABS produces a range of data on the economic wellbeing of migrants. This includes:
    • Migrant Data Matrices (3415.0) – provide information from a range of ABS data sources including personal and household finances from SIH and HES and wellbeing measures from the GSS. Data are updated annually.
    • Understanding Migrant Outcomes - Enhancing the Value of Census Data, Australia, 2011 (3417.0). Records from the Department of Immigration and Border Protection Settlements Database (SDB) are linked to ABS 2011 Census data. Included are data on employment and income of migrants by migration stream (e.g. Skilled, Family and Humanitarian), whether primary or secondary applicants and whether they had applied onshore or offshore.

    Disabled people and carers

    The Survey of Disability, Ageing and Carers (SDAC) is a cross-sectional survey conducted every three years in both private and non-private dwellings. Data are available for 1993, 1998, 2003, 2009 and 2012. Includes long-term health conditions and care requirements (including for older people), financial impacts on carers and income.

    The latest output is Disability, Ageing and Carers, Australia: Summary of Findings, 2012 (4430.0). A CURF will also be available in early 2014.

    Aboriginal and Torres Strait Islander peoples

    The National Aboriginal and Torres Strait Islander Social Survey (NATSISS) is a cross-sectional survey conducted every six years. Data are available for 1994, 2002 and 2008.

    Data are collected from Aboriginal and Torres Strait Islander peoples in private dwellings in both remote and non-remote areas, including income and finances, work, housing and mobility and financial stress.
      The latest output is National Aboriginal and Torres Strait Islander Social Survey, 2008 (4714.0). A CURF is also available.
        Box 1. International comparisons - Luxembourg Income Study (LIS)

        The LIS is a cross-national data centre consisting of two databases:

          • Luxembourg Income Study Database
          • Luxembourg Wealth Study Database.

        The LIS harmonises micro data to enable international comparisons. Income data is available from 45 countries and wealth data from 12 countries. Registered researchers can apply for remote access to the data for non-commercial purposes.

        For more information: <www.lisdatacenter.org>

      Integrating administrative data to measure economic wellbeing

      Statistical data integration involves bringing together data from different sources at the unit level (e.g. for an individual person or organisation) or micro level (e.g. information for a small geographic area) to enable analysis of a combined set of information for statistical and research purposes.

      Analysis of integrated administrative and other data offers valuable opportunities to investigate more complex and expanded policy and research questions than would be possible using only separate, unlinked data sources. As the data is already collected for an administrative purpose, it can be used without imposing additional burden.

      Administrative datasets provide opportunities for both cross-sectional analysis of society, small areas and subpopulations of interest, along with longitudinal analysis of the circumstances of individuals, households or families.

      Diagram 1 shows how administrative data sources relate to the concepts of household economic wellbeing.

      Diagram 1. Household economic wellbeing measures and relationships based on administrative data

      Household economic wellbeing measures and relationships based on administrative data


      Changes in the levels and distribution of economic resources in a society over time are key concerns of social and economic analysts. This fact sheet presents time series analysis of the three dimensions of household economic wellbeing – income, consumption and wealth.

      The analysis uses data from the Survey of Income and Housing (SIH), Household Expenditure Survey (HES) and Household Income and Labour Dynamics in Australia Survey (HILDA).


      Income data has been collected in the HES since 1984 and in the SIH since 1994–95.

      Since 1994–95, median equivalised disposable household income (EDHI) has increased in real terms from $505 to $790 (up 56%). Low income households have had a slightly lower real increase in their average income (47% at top of P10) than high income households (60% at top of P90). (Graph 1)

      Graph 1. Equivalised disposable household income at top of selected percentiles, 1994–95 to 2011–12(a)
      EDHI increased in real terms for each percentile but increased more for P80 and P90 than P10 and P20
      (a) In 2011-12 dollars, adjusted using changes in the Consumer Price Index
      Source: ABS Survey of Income and Housing (6523.0)

      Average wages and salaries and government pensions and allowances both increased significantly in real terms between 1994–95 and 2011–12 (52% and 24%, respectively).

      The Gini coefficient is a single statistic between zero and one and is a summary indicator of the degree of inequality, with values closer to 0 representing less inequality, and values closer to one representing greater inequality. Since 1994–95, the Gini coefficient for EDHI has been lowest in 1996–97 (0.292) and highest in 2007–08 (0.336). It decreased by 5% between 2007–08 and 2011–12. (Graph 2)Graph 2. Gini coefficient of equivalised disposable household income, 1994–95 to 2011–12
      Gini coefficient increased by 11% between 1994-95 and 2007-08 then decreased by 5% between 2007-08 and 2011-12
      Source: ABS Survey of Income and Housing (6523.0)

      The HILDA survey provides valuable insight into economic circumstances over time and the persistence of income disadvantage for individual households. Two thirds of households with a low income (lowest two income quintiles) in 2001 continued to have a low income in 2009. Similarly, over 60% of high income households (highest two quintiles) in 2001, remained at the top of the income distribution in 2009. (Graph 3)

      Graph Image for Graph 3. Comparison of income levels in 2001 and 2009

      Improvements in the SIH since 2003–04

      The ABS has implemented improvements to the SIH to ensure the survey accurately measures the distribution of economic resources among households in Australia, including:
      • Integration of the SIH with the HES
      • Computer assisted personal interviewing (CAPI) introduced
      • Sample design improved
      • Extra income questions (incl. non-cash and irregular income; salary sacrificed income specifically collected)
      • New benchmarking methods
      • Wealth data and imputed rent for first time
      • Further improvements to income incl. lump sum payments, financial support from family and trusts
      • Implementation of new income definition incl. recompiling 2003–04 and 2005–06 where possible
      • Wealth data every SIH
      • SIH income and wealth comparison with Australian System of National Accounts (ASNA) published in appendices of 6523.0 and 6554.0
      • Social transfers in kind (STIK) allocated in every SIH
      • Previous HES only items incl. disability and health care cards in every SIH to improve STIK allocations
      • More detailed superannuation information
      Graph 4 shows the impact of improvements in the measurement of income introduced in SIH 2007–08 and recompiled where data was available for 2003–04 and 2005–06. The improvements had most impact on households at the top of the income distribution, mainly from wages and salaries. In 2011–12, the EDHI for households at the top of P90 was 8% higher than the previous income definition while in 2005–06 it was 4% higher. At the top of P10 the changes increased EDHI by 1% in 2011–12, while mean weekly income increased by 3% in 2005–06 and 6% in 2011–12.Graph 4. Equivalised disposable household income, current and previous income definition(a)
      Current income definition introduced in 2007-08 increased mean EDHI by 3% compared to previous definition, and by 6% at P90 and 1% at P10
      (a) In 2011-12 dollars, adjusted using changes in the Consumer Price Index
      Source: ABS Survey of Income and Housing (6523.0)

      Consumption expenditure

      As incomes have risen, consumption expenditure has also risen. Between 1984 and 2009–10 average weekly expenditure of all households increased in real terms by one third from $933 to $1,236. The increase in expenditure was greatest for households in the fourth and fifth gross income quintiles. In these quintiles average income exceeded average consumption expenditure by 14% and 31%, respectively in 2009–10. By comparison, households in the lowest two income quintiles had average expenditure higher than their average disposable income. (Graph 5)

      Graph Image for Graph 5. Average expenditure and disposable income, by gross income quintile, 1984 and 2009-10

      Footnote(s): (a) In 2009-10 dollars, adjusted using changes in the Consumer Price Index

      Source(s): ABS Household Expenditure Survey (6530.0)

      Consumption patterns of households have changed since 1984. Current housing costs increased from 13% of total household expenditure on goods and services in 1984 to 18% in 2009–10. The proportion of expenditure on food and non-alcoholic beverages declined gradually in the same period (from 20% to 17% of total consumption expenditure), while spending on clothing and footwear almost halved (from 7% to 4% of total). (Graph 6)

      Graph 6. Proportion of total goods and services expenditure, selected groups, 1984 to 2009-10
      In 1984 average household expenditure on food and non-alcoholic beverages exceeded all other expenditure categories. In 2009-10, current housing costs was highest
      Source: Household Expenditure Survey (6530.0)

      Improvements in the HES since 1998–991998–99
      • Household Expenditure Classification (HEC) replaced HES Commodity Code List for classifying expenditure
      • Financial stress indicators collected for first time
      • HES and SIH integrated (HES for a subsample of SIH respondents)
      • Expenditure, income, wealth and financial stress available for all HES households
      • Non-cash benefits from employers included in consumption expenditure
      • Expenditure also classified by the international Classification of Individual Consumption by Purpose (COICOP)
      • Extra metropolitan sample of households with main source of income government pensions and allowances added to HES for development of a Pensioner and Beneficiary Living Cost Index
      • HES expenditure comparison with the ASNA published in Appendix 3 of 6530.0


      The distribution of wealth in Australia is less equal than income. Comprehensive information on the composition of the assets and liabilities held by households has been collected in the SIH since 2003–04. Previously, the value of owner occupied dwellings and loans on those dwellings were the only wealth data collected in these surveys.

      Median net worth has increased in real terms from $369,000 in 2003–04 to $434,000 in 2011–12. The average net worth of high wealth households has increased by more than the net worth of low wealth households e.g. the net worth of households at the top of the fourth quintile (P80) increased by 25% (to $1m) while the net worth of households at the top of the lowest quintile (P20) increased by 12% (to $88,000) in the eight year period to 2011–12. (Graph 7)

      Graph Image for Graph 7. Household net worth at top of selected percentiles, 2003-04 to 2011-12(a)

      Footnote(s): (a) In 2011-12 dollars, adjusted using changes in the Consumer Price Index (b) Wealth data not available for 2007-08

      Source(s): ABS Survey of Income and Housing (6554.0)

      The composition of assets has remained relatively stable between 2003–04 and 2011–12. Property assets (own dwelling and other property) comprised just under 60% of total assets in both years, although there was a slight reduction in the proportion for owner occupied dwellings offset by a small increase in other property. Superannuation rose from 12% to 15% of total household assets in the same period. (Graph 8)

      Property loans made up a slightly higher proportion of liabilities in 2011–12 (90%) than in 2003–04 (86%).

      Graph Image for Graph 8. Composition of assets, 2003-04 to 2011-12(a)

      Footnote(s): (a) In 2011-12 dollars, adjusted using changes in the Consumer Price Index

      Source(s): ABS Survey of Income and Housing (6554.0)

      Demographic Changes

      When analysing the distribution of household economic resources over long time periods, changes to the population’s age profile, their sources of income and household composition can impact on wellbeing measures.

      In the period from 1994–95 to 2011–12, average household size has fallen by 4% mainly due to a 14% fall in the average number of dependent children. In the same period the average number per household of persons 65 years and over, and of employed persons increased by 13% and 7%, respectively. (Graph 9)

      The ABS has undertaken analysis of the impact of demographic changes on measures of income inequality and found that about one third of the total increase between 1994–95 and 2002–03 could be explained by demographic factors.

      Graph Image for Graph 9. Average number of persons in household, percent change 1994-95 to 2011-12


      CAPI/CATI – Computer assisted personal interview or computer assisted telephone interview (CATI)

      Confidentialised unit record file (CURF) – a file containing micro data where the confidentiality of records is preserved using statistical techniques

      Cross-National Equivalent File (CNEF) – a file containing equivalently defined variables for panel studies from several different countries

      Cross-sectional survey – the sample for the survey is selected at a point in time

      Deciles/Quintiles – groupings that result from ranking households by economic resource and then dividing the population into ten equal groups (deciles) or five equal groups (quintiles)

      Disposable income – total income, monetary and in kind, less income tax, the Medicare levy and the Medicare levy surcharge

      Equivalisation – a method of standardising the income, expenditure or wealth of households to take account of household size and composition differences

      Household – a person living alone or a group of related or unrelated people who usually live in the same private dwelling

      Imputed rent – allows more meaningful comparisons of the economic wellbeing of people living in different housing tenures by imputing income based on the difference between market rent and actual housing costs for owner occupiers and subsidised private renters

      Longitudinal survey – the same sample units are revisited for multiple survey periods allowing analyses of individuals over time

      Social transfers in kind – goods and services provided to households free or at subsidised prices by governments e.g. for education, health, housing and child care

        Australian Bureau of Statistics (ABS), 2005, Research paper: Impact of Demographic and Economic Changes on Measured Income Inequality, (1351.0.55.005), ABS, Canberra

        ABS, 2009, ABS Data Quality Framework, (cat no. 1520.0) ABS, Canberra

        ABS, 2009, Household Income and Income Distribution, Australia 2007–08, (6523.0), Appendix 4: Improvements to income statistics, ABS, Canberra

        ABS 2011, Household Wealth and Wealth Distribution, Australia, 2009–10, (cat. no. 6554.0), Feature article: Low economic resource households, ABS, Canberra

        ABS 2012, Household Expenditure Survey and Survey of Income and Housing, User Guide, 2009–10, (cat no. 6503.0), ABS, Canberra

        Data integration: see “data integration” on the ABS website or “statistical data integration” on the National Statistical Service website

        McLachlan, R., Gilfillan, G. and Gordon, J. 2013, Deep and Persistent Disadvantage in Australia, Productivity Commission Staff Working Paper, Canberra

        OECD, 2013, OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth, OECD Publishing

        OECD, 2013, OECD Guidelines for Micro Statistics on Household Wealth, OECD Publishing

        United Nations, 2011, Canberra Group Handbook on Household Income Statistics, Second Edition, United Nations Economic Commission for Europe

        Wilkins, R., Warren, D., 2012, Families, Incomes and Jobs, Volume 7, Melbourne Institute of Applied Economics and Social Research, Melbourne