The data underpinning the indexes

This chapter looks at the data used to construct the four indexes in SEIFA 2021. All data is from the 2021 Census of Population and Housing.

The candidate variable list from SEIFA 2016 was used for SEIFA 2021 with one exception: the dwelling internet connection variable was not included in Census 2021, and therefore was not available for inclusion in SEIFA 2021. The candidate variables fall into a multi-dimensional framework. The dimensions are:

• income
• education
• employment
• occupation
• housing
• miscellaneous.

Variables typically relate to persons but can also relate to families or dwellings.

The variables were expressed as proportion of units in an area with a specific characteristic. Depending on the variable, the unit may be a person, family, or dwelling. As each variable was expressed as a proportion, a numerator and denominator were required. The numerator for each variable was a subset of the denominator. In most cases, the numerator and denominator specifications were based on SEIFA 2016 specifications. Some minor changes were made to reflect updates to the Census 2021 variable coding. The Appendix contains detailed descriptions of the numerators and denominators used for all the SEIFA variables. Note that for convenience of presentation in the following sections, the variable proportions are expressed as percentages.

A person may or may not be enumerated at their place of usual residence on Census Night. Where possible for SEIFA 2021, a person's usual residence was used as the basis of analysis. Counts compiled on a ‘place of usual residence’ basis are appropriate for SEIFA, because they are less likely to be influenced by seasonal factors such as school holidays and snow seasons. However, it is important to understand that certain areas, for example SA1s in popular tourist destinations, may receive scores influenced by the specific time at which the Census is conducted. For instance, the 2021 Census was conducted in August 2021, which is during the high season for ski resorts and the townships in those areas. This means that these areas may have higher property rental prices, higher employment figures and greater income levels than if the Census were conducted in the low season.

We excluded records with ‘Not stated’ and ‘Not applicable’ values (for the particular variable) from both the numerator and denominator counts. Overseas visitors were excluded implicitly by using usual residence summation, and explicitly in the few instances where this was not possible. For details, see the Appendix.

The numerator and denominator values were calculated from confidentialised Census counts, with the confidentialisation process being the same as that used for the TableBuilder product and other Census releases. Where necessary, the derived proportions were adjusted so that none of them were less than zero or greater than one.

This section contains a description of each variable on the candidate variable list. There is a brief discussion of how each variable relates to our definition of relative socio-economic advantage or disadvantage. 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.

Income is an important economic resource and is a core component of our notion of relative socio-economic advantage or disadvantage. Income variables are used in all the SEIFA indexes except the Index of Education and Occupation. The income variables are constructed using equivalised household income. Equivalisation is a process in which household income is adjusted by an ‘equivalence scale’, based on the number of adults and children in the household. The SEIFA variables using equivalised household income are calculated from the Census 2021 Equivalised Total Household Income variable (HIED).

The low income variable has been defined for SEIFA 2021 to capture approximately the first and second deciles of the equivalised household income distribution, excluding negative and nil income. That is, those people living in dwellings with equivalised household income between $1 and $499 per week ($1 to $25,999 per year). While the first quintile of equivalised household income was a strong indicator of disadvantage, people reporting negative and nil incomes tended to have profiles with less association with disadvantage. The cut-off of $91,000 for the high income variable was chosen to approximately capture the highest income quintile (top 20%).

Education is important when considering socio-economic advantage and disadvantage because the skills people obtain through school and post-school education can increase their own standard of living, as well as that of their community. Certificate Levels I and II are regarded as a lower educational attainment than year 12 schooling, and are grouped in the NOYR12ORHIGHER variable, as opposed to the CERTIFICATE variable. This specific educational hierarchy was based on the ABS publication Education and Work Australia. Note also that the CERTIFICATE variable is an indicator of relative disadvantage in SEIFA. It is true that having a certificate qualification gives a person an advantage over someone with no qualifications. However, at an area level, a high proportion of people with certificate qualifications correlates with other disadvantaging characteristics (e.g. lower skilled occupations).

For most people, employment is their main source of income. Employment can also contribute to social participation and self-esteem. An unemployment variable is included in each of the SEIFA indexes. The standard unemployment variable (UNEMPLOYED) is calculated as the number of unemployed people divided by the number of people in the labour force (the unemployment rate). The variable used in the Index of Economic Resources (UNEMPLOYED_IER) is the number of unemployed people divided by the entire adult population of the area. This enables us to distinguish the unemployed from those employed and those not in the labour force, as the latter two groups were found to have significantly higher average wealth.

Occupation plays a significant part in determining socio-economic advantage and disadvantage. The ability to accumulate economic resources varies greatly with occupation type. The SEIFA 2021 occupation variables have been classified using the Australian and New Zealand Standard Classification of Occupations, Version 1.3 (ANZSCO).

Each occupation in ANZSCO is assigned a skill level ranging from 1 (highest) to 5 (lowest), which indicates the range and complexity of the set of tasks performed in a particular occupation. These skill levels were used as the basis of the occupation variables in the Index of Education and Occupation. For the purposes of OCC_SALES_L and OCC_SERVICE_L, low skill was determined as skill levels 4 and 5. The aim was to include broad categories of both advantaging and disadvantaging occupations, which complement the education variables by introducing the aspect of vocational skills. For the IRSD and the IRSAD, we used the ANZSCO major groups in conjunction with the skill levels to construct the occupation variables. This was done to identify occupations, or groups of occupations, which contribute to relative advantage or disadvantage at an area level. Using the major groups as well as the skill levels also helped to maintain consistency with SEIFA 2016.

Having an adequate and appropriate place to live is fundamental to socio-economic wellbeing. There are many aspects to housing that affect the quality of people’s lives. Dwelling size, cost and security of tenure are all important in this regard, and are therefore considered in SEIFA. Housing size is measured by the variables FEWBED, HIGHBED, OVERCROWD and SPAREBED. The variable FEWBED measures dwellings with one or no bedrooms, whilst the variable HIGHBED measures dwellings with four or more bedrooms. The variable OVERCROWD measures dwellings that do not have enough bedrooms for their occupants. Conversely, the variable SPAREBED measures dwellings that have one or more bedrooms spare for their occupants. These last two variables are calculated using the Canadian National Occupancy Standard, which 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, 2019-20. Housing cost for SEIFA is measured using reported mortgage or rent payments. The cut-offs for the high and low groups were based on the ranges corresponding to the top and bottom quintiles. The high housing cost variables (HIGHMORTGAGE, HIGHRENT) are indicators of relative advantage, because they indicate greater financial capacity, as well as higher quality housing or locational advantage.

The low housing cost variable (LOWRENT) is an indicator of relative disadvantage, for similar reasons.

Owning a house, with or without a mortgage, is an indicator of advantage. First, owning a house implies security of tenure. For many Australian households, the family home is their most valuable asset. Owning with a mortgage indicates the financial capacity to make repayments, as well as the possession of a future asset. The denominator of the mortgage and rent variable proportions is based on all households in an area.

The Census captures limited household information, and does not for instance capture housing affordability, housing stress, dwelling value and dwelling quality. Although some variables, such as number of bedrooms and amount of rent or mortgage payments, may provide a proxy in some instances, their relationship to dwelling quality and dwelling value is not uniform across all areas.

An investigation using SEIFA 2016 was conducted on including housing stress, as defined by housing costs comprising 30% or more of the total household income, for lower income households only. The analysis showed that the impact on the overall distribution of SEIFA scores was small, and it was noted that the definition of housing stress had limitations.

The CHILDJOBLESS variable is defined as the proportion of families with children under 15 years old and jobless parents. The variable could be an indicator for entrenched disadvantage since children who grow up in jobless families may be more likely to experience intergenerational unemployment and diminished opportunities to participate in society.

The disability variable (DISABILITYU70) provides an indication of the physical or health aspects of socio-economic disadvantage. It is based on the Census question on need for assistance, which was developed to provide an indication of whether people have a profound or severe disability. People with a profound or severe disability are defined as those people needing help or assistance in one or more of the three core activity areas of self-care, mobility and communication, because of a disability, long term health condition (lasting six months or more) or old age. Disability limits employment opportunities, and possibly access to community resources. For the purpose of indicating relative socio-economic disadvantage, we have limited the scope of the SEIFA disability variable to people aged under 70, as was done for SEIFA 2016.

Questions relating to long-term health conditions were asked for the first time in Census 2021. These were not added to the SEIFA candidate variables for 2021, as many health researchers are interested in measuring individual health outcomes and analysing their relationship with socio-economic advantage/disadvantage. If SEIFA included health variables, it would make these relationships less clear and significantly harder to interpret. It was determined that it would be beneficial to retain the established approach to SEIFA, which is to only include the DISABILITYU70 variable.

A lack of fluency in English may limit employment opportunities and the ability to participate in society.

A car is both a material resource and a means of transport that enables greater freedom. A limitation of the NOCAR variable is that the need for a car varies depending on the remoteness of the area and access to public transport.

A past analysis of wealth data collected by the ABS showed that lone person households have lower average wealth (per person) than other household types. A higher proportion of lone person households in an area is correlated with lower ability to access economic resources beyond what is measured by the equivalised household income variables. An analysis of group households yielded a similar conclusion – an association with low wealth. A high proportion of unincorporated enterprise owners was found to correlate with high wealth and access to economic resources. These three variables were used only in the Index of Economic Resources.

One parent families are disadvantaged compared with other family structures, because of the need to simultaneously provide and care for dependants. Aside from having lower equivalised household incomes, one parent families also have lower rates of employment and labour force participation, lower rates of home ownership and higher incidence of financial stress, as compared to couple family households – for example, refer to Australian Social Trends, 2007. There are significant correlations at the area level between the number of one parent families and many indicators of relative socio-economic disadvantage. The same patterns are evident for areas with high proportions of people who are separated or divorced.

The Census data was converted into the SEIFA variable proportions. Summary statistics for these proportions were analysed to identify significant changes since 2016. Overall, there were no unexpected changes to the SEIFA variable proportions.

The following table shows the candidate variable list for each index. The candidate list includes all variables considered for inclusion in an index before the principal component analysis stage. The final list of variables included in each index can be found in in technical details of each index: variables and loadings.