For IHAD 2021, the concept of household level socio-economic advantage and disadvantage is the same as that used in the Experimental IHAD 2016. That is, the ABS broadly defines relative household socio-economic advantage and disadvantage in terms of the individual access to resources of people living within households and their ability to collectively share these resources in order to participate in society (Wise and Williamson, 2013). This concept is described as ‘broadly defined’ in recognition of the many concepts that have emerged in the literature to describe advantage and disadvantage.

In most households, members often pool their income and resources and share similar living characteristics. A household can still be advantaged overall as a unit even if it contains some less advantaged members. This support isn’t restricted to economic aspects; for example, children within households containing people with higher levels of education may be more advantaged in educational outcomes than children in households containing people with lower levels of education. 

Since relative socio-economic advantage and disadvantage is a complex and multidimensional concept, it is difficult to condense into a single index with a manageable, accessible framework. The limitations of the data collected in the Census also place restrictions on the scope of the notion of advantage and disadvantage available to be used. The most important elements covered by SEIFA, the IHAD, and other socio-economic indexes developed around the world, include income, education, employment, occupation, housing, and family structure. Variables have been selected from these dimensions and are discussed further in the Description of candidate IHAD variables.

An important point to consider is both what is measured by a socio-economic index, and what is not measured. This helps clarify the ability of the index to accurately measure household relative socio-economic advantage and disadvantage (Wise and Mathews, 2011). Defining the concept behind IHAD provides more information on the index. 

This section gives a description of the concept behind IHAD. For a list of the variables included, refer to the Technical details for IHAD: variables and loadings.

The IHAD summarises information about the economic and social conditions of people within households, including both relative advantage and disadvantage measures.

Household scores are created by adding together the weighted measures of the characteristics of that household. The scores for all households are then standardised to a distribution where the mean equals 1,000 and the standard deviation is 100.

A low score indicates relatively greater disadvantage and a lack of advantage in general. A high score indicates a relative lack of disadvantage and greater advantage in general. 

Scores are an ordinal measure on an arbitrary scale and do not represent the quantity of advantage or disadvantage. Therefore, it is not accurate to say a household with a score of 1,000 is twice as advantaged as a household with a score of 500.

The individual household level IHAD scores are not available as outputs. Quantiles (deciles and quartiles) have been provided for analytical purposes.

Every household is ordered from lowest to highest score, with the lowest 10 per cent of households given a decile number of one, the next lowest 10 per cent of households given a decile number of two and so on, up to the highest 10 per cent of households which are given a decile number of 10. This means that households are divided up into 10 equal sized groups, based on their score, with decile 1 representing the most disadvantaged households and decile 10 representing the most advantaged households. In practice these groups won’t each contain exactly 10% of households as it depends on the distribution of the IHAD scores. Note that the groups will have an approximately equal number of households, not an approximately equal number of persons.

Every household is ordered from lowest to highest score, with the lowest 25 per cent of households given a quartile number of one, the next lowest 25 per cent is given a quartile number two and so on, up to the highest 25 per cent of households which are given a quartile number of 4. This means that households are divided up into four equal sized groups, based on their score, with quartile 1 representing the most disadvantaged households and quartile 4 representing the most advantaged households. In practice these groups won’t each contain exactly 25% of households as it depends on the distribution of the IHAD scores. Note that the groups will have an approximately equal number of households, not an approximately equal number of persons.

This chapter looks at the data used to construct IHAD 2021. All data is from the 2021 Census of Population and Housing.

Variables from the Census were included in the initial candidate variable list for IHAD if they were deemed to be related to the definition of advantage and disadvantage that the IHAD is intending to capture. The same candidate variable list from the Experimental IHAD 2016 was used for IHAD 2021, excluding the dwelling internet connection variable as noted in Variables underpinning IHAD. The candidate variables fall into a multi-dimensional framework. The dimensions are: 

• housing,
• family,
• education,
• occupation, and
• miscellaneous.

IHAD is constructed from 2021 Census data, with variables derived as binary indicators. Variables typically relate to persons but also relate to families or dwellings. Family and person level variables have been derived at the household level. For example, for the candidate variable ‘households where the person with the highest educational attainment has a Bachelor Degree or above’, the highest qualification for all in scope persons in the household is considered and if one person has a Bachelor Degree or higher, the derived variable has a value of 1. If no people in the household have a Bachelor Degree or higher, the value is 0. For the candidate variable ‘households where all people aged 15 years and over are unemployed’ if all in scope people aged 15 years and over in the household have labour force status unemployed, the derived variable will have a value of 1, otherwise it will have a value of 0. In most cases, the indicator specifications were based on the Experimental IHAD 2016 specifications. Some minor changes were made to reflect updates to the Census 2021 variable coding. The Appendix contains detailed descriptions of the indicator specifications used for all the IHAD variables. 

The scope of the IHAD is private dwellings that were occupied on Census Night. Non-classifiable occupied private dwellings (e.g. dwellings that only contained visitors) and unoccupied private dwellings were excluded. This accounted for approximately 1.6 million dwellings or 14.5% of all private dwellings. Approximately 1.0 million (9.6%) were unoccupied private dwellings; 0.5 million (4.9%) were non-classifiable occupied private dwellings. Non-private dwellings, offshore, migratory, and shipping were also excluded. Note that residents in boarding houses and hostels are not included as these are classified as non-private dwellings.

The IHAD is calculated based on the characteristics of persons who are both usually resident in a household and enumerated in that household on Census Night. If all usual residents of a household aged 15 or more were away from home on Census Night, that dwelling would be out of scope of the IHAD.

Persons temporarily overseas on Census Night are out of scope of the Census, and thus Census data is not available for those persons. Persons staying elsewhere in Australia are in scope of the Census, but they are not able to be associated back to their dwelling of usual residence, and therefore their characteristics as measured in the Census are not able to be used in the derivation of the household level variables used in the index.

If one or more usual residents were away, but at least one person was at home on Census Night, then that dwelling remains in scope of the IHAD and an index value would be calculated for that household. However, the persons temporarily away from that dwelling would not have their characteristics contributing to the index value for that household; only those persons present will. This may result in a different level of advantage or disadvantage being calculated for that dwelling than would have been the case had all persons usually resident in that dwelling been at home on Census Night. Around 4.8% (0.44 million dwellings) of in scope dwellings had one or more usual residents away from home on Census Night.

An example of this situation would be a one family couple household with two adults usually resident. One member of the couple is unemployed and was at home on Census Night, while the other person was travelling for work-related reasons. The person characteristics used in the calculation of the IHAD are based only on the characteristics of the (unemployed) person that was home on Census Night.

Rules for the minimum number of persons and dwellings for an area to receive an index score has been a feature of SEIFA since its inception following the 1986 Census. In the Statistical Areas Level 1 (SA1) and Statistical Area Level 2 (SA2) data cubes available in the Data downloads section, IHAD quartile percentages will not be provided for SA1s or SA2s that do not have a SEIFA score. Refer to Areas without a SEIFA score for more information about these excluded areas.

Data quality considerations for construction of an index at household level centre on the level of non-response to Census questions. Overall non-response was relatively low (around 0-6%) and fairly consistent across candidate variables, with the exception of equivalised total household income (HIED and level of highest educational attainment (HEAP) (around 7-11%). Please refer to the Census of Population and Housing: Census Dictionary, 2021 for details about these variables.

Due to partial non-response from some Census respondents, some households could not be included in the IHAD construction without some action to account for missing values within candidate variables. For example, for the candidate variable ‘Households where all people aged 15 years and over are unemployed’, if someone within the household does not indicate their labour force status, it may not be possible to assign a value for this candidate variable.

Two actions to deal with missing data have been applied:

• removal of households with high numbers of non-response
• imputation of missing values

Households with 10 or more missing candidate variable responses have been removed. This number is consistent with the Experimental IHAD 2016 and was chosen because it tended to correspond to dwellings where most person-based variables were coded as 'not stated' (Wise and Williamson, 2013). Approximately 0.5% of in scope households (43,726) were removed; the proportion of households with 3 or fewer missing candidate variable responses was approximately 97.5%.

Wise and Williamson (2013) noted that if ‘not stated’ responses are grouped with records that do not have a particular disadvantaging characteristic, then there is an implicit advantage being assigned to those individuals. They recommended that imputation should be performed where appropriate.

Missing values for household, family, and person level Census variables that were required have been imputed. The method used randomly assigned missing responses for a given variable to one of the allowed responses, based on the frequency proportions for the variable at the national level. As a result, the distribution of the imputed responses for most of the variables being treated aligned within reasonable bounds with the original distribution of non-missing responses.

This was also true for HIED (equivalised total household income) at the national level. However, within household composition categories the distribution of imputed values was more variable. By state, the rate of non-response for HIED was between 5-11%; when looking at different household compositions it ranged from 3-19%, with group and multiple family households having the higher values within that range. Similar results were observed for HEAP (level of highest educational attainment) by state and by ten-year age groups.

Due to this variability and consistent with the Experimental IHAD 2016, hot-deck imputation was applied to HIED and HEAP. This involved assigning a value for the missing HIED/HEAP variable from a donor that matched the recipient’s values for a selection of other Census variables. For HIED the selection of variables were household composition, state or territory, SA2, and number of adults and employed people in the household; for HEAP the selection of variables were: age in 10 year groups, state or territory, SA2, level of non-school qualification, highest year of school completed, total personal weekly income and labour force status. The distribution of the imputed responses for each of the variables improved from the initial imputation approach and was deemed to be sufficient for the purpose of constructing the IHAD.

This section contains a description of each variable on the candidate variable list. The tables containing the variable descriptions also state whether the variable is an indicator of relative advantage (adv) or relative disadvantage (dis). Each subsection corresponds to one of the socio-economic dimensions listed in The candidate list of variables. The candidate list includes all variables considered for inclusion in IHAD before the principal component analysis (PCA) stage. The final list of variables included in IHAD can be found in the Technical details for IHAD: variables and loadings.

List of household variables

List of family variables

List of person variables - education

List of person variables - occupation

List of person variables - miscellaneous

This chapter describes the methods used to construct IHAD, some important technical specifications and basic outputs.

Principal component analysis (PCA) has been used since the first release of SEIFA to summarise Census variables related to socio-economic advantage and disadvantage. The same methodology is used to create the IHAD, modified where necessary to use binary variables.. The aim of PCA is to reduce a large number of correlated variables into a smaller set of transformed variables, called "principal components". Each component is a weighted linear combination of the original. It is possible to extract as many components as there are variables. If the original variables are highly correlated, much of the variation can be summarised by a single principal component.

The first principal component is the weighted linear combination of variables that captures the maximum amount of variation present in the original dataset. This is calculated using the correlations between the variables. In general, variables that are strongly correlated with many others in the list will receive high weights. The first principal component is used to create the IHAD index.

The PCA used the binary candidate variables and the correlation matrix of these variables to give an indication of how significantly each variable contributes to the measurement of the unobserved latent variable of interest, namely socio-economic advantage and disadvantage. Each variable receives a loading that indicates the correlation of that variable with the index. A positive loading indicates an advantaging variable whereas a negative loading indicates a disadvantaging variable. The variables with the highest loadings are the ones that have the highest correlation with the index value.

Polychoric correlations were used instead of the standard Pearson correlations for the correlation matrix; this is appropriate for binary variables to ensure the correlation coefficients used in the PCA are unbiased. Using polychoric correlations is considered to be more accurate when running a PCA on discrete data such as the binary variables used in the IHAD.

The candidate variables listed in Description of candidate IHAD variables were used in the PCA for the IHAD and removed if their loading was less than or equal to 0.3 on the grounds that they were not particularly strong indicators of advantage or disadvantage. This process was performed iteratively, until all of the variables had a loading above 0.3. This is the same procedure used to create the SEIFA. The final variables and their loadings following this process are presented in the Technical details for IHAD: variables and loadings.

The first principal component scores were derived by taking the product of each standardised variable with its respective weight, then taking the sum. For convenience and consistency with the approach taken for SEIFA, these raw component scores were then standardised to a mean of 1,000 and a standard deviation of 100 to produce the index.

The sign of the PCA weights is arbitrary, but intuitively we want more disadvantaged households to have lower scores, for example NOCAR is a disadvantage variable and so should have a negative weight. The weights were multiplied by -1 to give advantage indicators positive weights and loadings, and disadvantage indicators negative weights and loadings. Accordingly, high index scores indicate relative advantage, and low index scores indicate relative disadvantage.

With the preceding two sections providing context, a step-by-step process for constructing IHAD is presented below:

Given the data available, we created a list of variables related to the definition of relative household socio-economic advantage and disadvantage.

We applied the IHAD scope to the dataset, and then identified households with 10 or more applicable missing responses. We removed these households from the dataset, imputed missing responses for most of the required variables, and then applied Hotdeck imputation for HIED and HEAP to create the dataset we used to construct the candidate variables.

We created binary indicators from household, family, and person level variables. These indicators take a value of 1 if the characteristics is present, and 0 if it isn’t.

We removed highly correlated variables to avoid over-representing any specific socio-economic characteristic. When two variables had a correlation coefficient greater than 0.8 in absolute value and were measuring conceptually similar aspects of advantage or disadvantage, we generally removed one of them. However, we applied some discretion, depending on the variables in question and the size of the correlation.

We conducted principal component analysis (PCA) using the binary candidate variables and the correlation matrix of these variables, to obtain the loading for each variable on the first principal component.

We excluded variables with loadings less than 0.3 in absolute value, on the grounds that they were not strong indicators of relative advantage or disadvantage. This limit is an accepted level in the PCA literature and has been used in past releases of SEIFA and IHAD. We removed variables one at a time, starting with the lowest loading variable.

We conducted a PCA on the reduced variable list, and if any other variables loaded below 0.3, we repeated steps six and seven.

This section gives the results of the principal component analysis carried out for IHAD, including variable loadings and percentage of variance explained. A list of variables initially considered for inclusion but removed due to high correlations with other variables or weak loadings is also provided.

IHAD summaries variables that indicate either relative socio-economic advantage or disadvantage, according to the concept described in Defining the concept behind IHAD. The final IHAD variables and loadings are listed below.

IHAD indicators of disadvantage

The following variables are indicators of disadvantage. PUBLIC_RENT is the strongest indicator of disadvantage in the index. 

IHAD indicators of advantage

The following variables are indicators of advantage. DEGREE_DEPENDENT is the strongest indicator of advantage in the index.

In most cases, highly correlated variables were removed from the initial candidate list. This was done to prevent instability in the variable weights and over-representation of any specific socio-economic characteristic. When two variables had a correlation coefficient of size greater than 0.8 in absolute value, one of them was generally removed. However, if they were deemed to be measuring different socio-economic characteristics (e.g. education and occupation), both were retained.

The following variables were initially considered for the index but were excluded when the analysis showed that they were weak indicators of relative advantage or disadvantage.

This section presents the frequency histogram of IHAD scores. The IHAD distributions have generally similar shapes to those from Experimental IHAD 2016.

The scores for IHAD range from 613 to 1,246; the table presents maximum and minimum scores of each IHAD quartile. These show that there is sufficient variation in the IHAD scores to allow for the formation of these groups.

Some households will not have any indicators of advantage or disadvantage (i.e. their values for the final binary candidate variables are all 0). They will still receive an IHAD score reflecting the middle of the IHAD score distribution, which places them in quartile 2.

Once the index was calculated, it was checked to ensure that it measured the desired concept and that the results make intuitive sense. This validation is important to establish the credibility of the index and identify any issues that may have been missed in the construction of the index. 

A range of validation checks were applied to the 2021 IHAD, including:

• comparison with the experimental IHAD 2016,
• comparison with 2021 SEIFA IRSAD, 
• sense checking of the SA1s with the highest and lowest proportions in quartile 1 or quartile 4, and 
• sense checking of SA1s where SEIFA IRSAD and IHAD differed the most. 

Results showed that IHAD 2021 is consistent with both the 2021 SEIFA IRSAD and the experimental IHAD 2016. The top and bottom SA1s were sensible and SA1s where IRSAD and IHAD differed the most were reasonable.  These checks confirmed that IHAD was performing as intended, in adding value to SEIFA IRSAD by showing the distribution of advantage and disadvantage at household level.

The IHAD is constructed at the household level, based on the assumption that economic and other resources are generally shared within households, and therefore persons within households will share similar levels of socio-economic advantage and disadvantage. However, this may not always be the case, particularly for multi-family households, group households, and households containing lodgers or boarders. It is possible for a relatively advantaged person to be a resident in a relatively disadvantaged household or a relatively disadvantaged person to reside in a relatively advantaged household.

Quantiles are created based on assigning, as near as is practicable, equal numbers of households into each quantile (rather than equal numbers of persons). As larger households tend to have higher index values, more advantaged deciles tend to contain larger numbers of persons.

IHAD is constructed using a weighted combination of selected variables. The index is dependent on the set of variables chosen for the analysis. A different set of underlying variables would result in a different index. However, due to the large number of variables in IHAD, removing or altering a single variable will usually not have a large effect.

Users should consider the aspect of socio-economic advantage and disadvantage in which they are interested and examine the underlying set of variables for IHAD. This will allow them to make an informed decision on whether IHAD is appropriate for their particular purpose. 

As the IHAD is constructed using Census variables, when undertaking analyses involving cross-tabulation of the IHAD with other Census variables, users should examine the variables contained within the index to aid in the interpretation of those results. Refer to Technical details for IHAD: variables and loadings for details of variables that were included in the IHAD.

The index is designed to compare socio-economic characteristics of a household at a point in time, not to compare households over time. There are several issues that make longitudinal or time series analysis of IHAD difficult to interpret.

• The constituent indicators and indicator weights for the index are likely to have changed.
• The distribution of the standardised index values will have changed (e.g., a score of 800 does not represent the same level of disadvantage in different years).
• Census variable changes will affect the variables used to calculate the IHAD scores.

Topics represented in IHAD are limited by the variables collected in the Census.

Measures relating to wealth, access to services, and social/community participation may provide more information about the relative advantage or disadvantage within a household, but these measures are not collected in the Census.

Long-term health conditions, asked for the first time in the 2021 Census, were not included in IHAD. This is to allow health researchers to analyse the relationship between health outcomes and socio-economic advantage/disadvantage. Adding health variables to IHAD would make these relationships less clear.

Other potential topics that could be associated with advantage and disadvantage but are not captured in the Census include crime, and the environment. If data were available on these topics, they may provide additional information about the level of advantage and disadvantage present within households that could result in households being assigned a different index value.

Mapping IHAD provides an excellent way of observing the spatial distribution of relative socio-economic advantage and disadvantage. Interactive maps at SA2 level are available for 2021 IHAD.

Australian Bureau of Statistics (2013) ANZSCO – Australian and New Zealand Standard Classification of Occupations, Version 1.3, cat. no. 1220.0, ABS, Canberra.

Australian Bureau of Statistics (Jul2021-Jun2026), Australian Statistical Geography Standard (ASGS) Edition 3, ABS Website, accessed 20 April 2023.

Australian Bureau of Statistics (2016) Census of Population and Housing: Census dictionary, ABS Website, accessed 23 August 2016.

Australian Bureau of Statistics (2021), Census of Population and Housing: Census dictionary, ABS Website, accessed 20 April 2023.

Australian Bureau of Statistics (Jun 2011), Information Paper: Measures of Socioeconomic Status, New Issue for June 2011, ABS Website, accessed 20 April 2023.

Joliffe, I.T. (1986) Principal Component Analysis, Springer–Verlag, New York.

Wise, P. and Mathews, R. (2011) Socio-Economic Indexes For Areas: Getting a Handle on Individual Diversity Within Areas, Methodology Research Papers, cat. no. 1351.0.55.036, Australian Bureau of Statistics, Canberra.

Wise, P. and Williamson, C. (2013) Building on SEIFA: Finer levels of Socio-Economic Summary Measures by Phillip Wise and Courtney Williamson (cat. no. 1352.0.55.135), Australian Bureau of Statistics, Canberra.

This appendix gives descriptions of each variable considered for inclusion for IHAD 2021. The square brackets contain specifications for creating the indicators from Census data items, according to the mnemonics used in the Census of Population and Housing: Census Dictionary, 2021. The variables are arranged by socio-economic dimension.

Notes:

• The Skill Level for each occupation can be found in ANZSCO – Australian and New Zealand Standard Classification of Occupations, Version 1.3
• Household composition was ‘not classifiable’ if the household: contained only visitors or persons aged under 15 years on Census night; or was determined to be occupied on Census Night but the collector could not make contact; or could not be classified because there was insufficient information on the Census form.
• The Canadian National Occupancy Standard determines housing appropriateness, using the number of bedrooms and the number, age, sex and relationships of household members. For more information refer to Housing Occupancy and Costs, Australia, 2019–20.
• Housing, Family, and Equivalised total household income variables listed below include only classifiable private dwellings [DWTD = 1 and HHCD = 1, 2 or 3]. Education, Occupation, and Miscellaneous variables listed below include only people living in classifiable private dwellings who were at home on Census night [DWTD = 1 and HHCD = 1, 2 or 3 and UAICP = 1].