Groups of people with different age structures tend to have different characteristics. For example, people in their 30s are likely to have a higher level of education than people in their 70s. Therefore, a neighbourhood with many people in their 30s is more likely to have a higher level of education in general than a neighbourhood with many people in their 70s. In SEIFA, a high level of education is considered to be relatively advantageous. This means that SEIFA ranks the younger neighbourhood as more advantaged, because of factors related to the different age profiles of the areas.
Age-standardisation could have been used to directly compare neighbourhoods with different age-profiles. However, age-standardisation was not used in SEIFA because the decision of whether to use age-standardisation depends on the use of SEIFA.
In SEIFA, adjusting for age was only undertaken for the variable measuring 'need for assistance'. This variable is directly linked to age because people are more likely to require assistance with core activities with increasing age. Some analysis would consider a 'need for assistance' to be important irrespective of age. However, SEIFA does not include people above the age of 70 in this measure, due to the impact this age group has on the variable.
Australian Standard Geographic Classification. For more information, please see Geography ASGC page.
Australian Statistical Geography Standard. For more information, please see Geography ASGS page.
CENSUS COLLECTION DISTRICT (CD)
The CD was the smallest data output area available in the Australian Standard Geographic Classification (ASGC). CDs were specifically designed for Census collection purposes and generally represented a reasonable workload for a Census collection officer.
CDs are no longer used by the ABS and therefore are not used in SEIFA 2011. The smallest data output geography for SEIFA 2011 is now the Statistical Area Level 1.
COMMONWEALTH ELECTORAL DIVISION
A Commonwealth Electoral Division (CED) is an area legally prescribed for the purpose of returning one or more members to the federal lower house of parliament. CEDs are approximated by aggregating the data for Statistical Areas Level 1 (SA1s) that best fit the area.
Commonwealth Electoral Divisions have different boundaries to State Electoral Divisions (SEDs), except in Tasmania and the ACT where they are the same. CEDs cover all of Australia.
Deciles divide a distribution into ten equal groups. In the case of SEIFA, the distribution of scores is divided into ten equal sized groups. The lowest scoring 10% of areas are given a decile number of 1, the second-lowest 10% of areas are given a decile number of 2 and so on, up to the highest 10% of areas which are given a decile number of 10.
The ABS defines relative socio-economic advantage and disadvantage in terms of “people’s access to material and social resources, and their ability to participate in society”.
The terms ‘disadvantage’ and socio-economic disadvantage’ are used interchangeably in this publication.
EQUIVALISED HOUSEHOLD INCOME
Equivalised household income is total household income adjusted by the application of an equivalence scale to facilitate comparisons of income levels between households of differing size and composition. The 'modified OECD' equivalence scale is used.
Equivalised total household income can be viewed as an indicator of the economic resources available to a standardised household. For a lone person household it is equal to household income. For a household comprising more than one person, it is an indicator of the household income that would be needed by a lone person household to enjoy the same level of economic wellbeing.
For more information, refer to the Census Dictionary.
Each variable in the analysis is correlated with each component of Principal Component Analysis (PCA). This correlation is called the loading. Loadings help to interpret what aspects of advantage and disadvantage a component may represent. The loadings are also useful in analysing the results from using different sets of original variables (such as for the four indexes in SEIFA). For more information on PCA and loadings, refer to the Technical Paper.
LOCAL GOVERNMENT AREA (LGA)
A Local Government Area (LGA) is an area under the responsibility of an incorporated local government or Indigenous council. LGA boundaries can be changed by the State/Territory government. LGAs are created using Mesh Blocks. For more information, see Diagram 2.
A mean is an average; a measure of central tendency of a distribution. A mean SEIFA score can be calculated by adding the value of all scores and dividing this by the number of scores being added.
MESH BLOCKS (MBs)
Mesh Blocks are the smallest area geographical region in the ASGS. There are approximately 340,000 covering the whole of Australia. They broadly identify land use into categories such as residential, commercial, agricultural and parkland. Residential and agricultural Mesh Blocks usually contain 30 to 60 households. Mesh Blocks are the building block for all the larger regions of the ASGS.
NEED FOR ASSISTANCE
The disability variable provides an indication of the physical or health aspects of relative socio-economic disadvantage. It is based on the need for assistance Census questions, which were 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, a long term health condition (lasting six months or more) or advanced age. For brevity in this paper, need for assistance is referred to using the term 'disability'. Note that the Census measure was designed to indicate the disability status of people in Australia according to geographic areas, and for small groups within the broader population, and is not a comprehensive measure of disability. Disability can limit employment opportunities, and consequently access to financial 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.
Percentiles divide a distribution into 100 equal groups. In the case of SEIFA, the distribution of scores is divided into 100 equal groups. The lowest scoring 1% of areas are given a percentile number of 1, the second-lowest 1% of areas are given a percentile number of 2 and so on, up to the highest 1% of areas which are given a percentile number of 100. SEIFA percentiles are provided to allow users to create their own groupings, such as quartiles (which contain 25% of SA1s).
SEIFA releases indexes for six different types of area: Statistical Area Level 1 (SA1s), Statistical Area Level 2 (SA2s), Statistical Local Areas (SLAs), Local Government Areas (LGAs), Postal Areas (POAs) and State Suburbs (SSCs). However, the SEIFA indexes can be used to create scores for other types of areas. Because SA1s form the basis of all of the standard geography boundaries, SA1s scores can be used to represent larger standard areas.
To create the scores for larger geographies, the SA1 scores are aggregated using a population weighting. To do this, first multiply the score of each SA1 within the larger geography by its usual resident population, and then divide the sum of these population-weighted SA1s by the total number of people within the larger area. Population counts for SA1s (the number of usual residents in the SA1s) have been provided with the index scores. From these scores, ranks, deciles and percentiles are calculated in the same manner as with SA1s.
It is important to note that, because of this method of construction, the distribution of scores for these larger geographic areas will not be a standard distribution. For example, the mean of the SLA scores will not be 1000, just as the standard deviation will not be 100. Also, the SLA deciles do refer to 10% of SLAs, and have only an indirect relationship to the SA1 deciles. An individual SLA will contain multiple SA1s, with a range of SA1 scores, ranks and deciles that will be different to the SLA score, rank and decile.
For more information refer to Chapter 4.3 of the Technical Paper.
POSTAL AREA (POA)
Postal Areas are not an ASGS standard geography but are based upon one or more SA1s in an attempt to match the postcodes used by Australia Post (at the time of the Census). Postcodes are used for delivering mail and in many cases have no specific boundaries. Because some surveys are based on postcode information rather than standard geographies, SEIFA is available for Postal Areas. However, SEIFA users need to be aware that Postal Areas and postcodes are not always good matches, and should use POAs with caution. For example, a SA1 can only be matched to a single Postal Area even if that SA1 spans two postcodes. The Postal Area number is the same as the matched postcode. They do not have names. There are 2516 POAs in the 2011 ASGS (of which, 2482 POAs were included in SEIFA).
To determine the SEIFA rank, all the areas are ordered from lowest score to highest score. The area with the lowest score is given a rank of 1, the area with the second-lowest score is given a rank of 2 and so on, up to the area with the highest score which is given the highest rank. While two areas may appear to have the same score due to rounding, every area has an individual score and an individual rank. However, caution should be used when separating areas with similar scores and ranks.
A SEIFA score is created using information about people and households in a particular area. A SA1 score is standardised against a mean of 1000 with a standard deviation of 100. This means that the average SEIFA SA1 score will be 1000 and the middle two-thirds of SEIFA scores will fall between 900 and 1100 (approximately). (Refer to Standardisation in the Glossary). A SEIFA score provides more information and is used for more sophisticated analysis. Ranks or deciles should be used for most analyses.
STATISTICAL LOCAL AREA (SLA)
Statistical Local Areas (SLAs) are made up of one or more SA1s. There are 1426 SLAs in the 2011 ASGS (of which, 1384 SLAs were included in SEIFA).
STATISTICAL AREAS LEVEL 1 (SA1s)
SA1s are the smallest region of the ASGS for which a wide range of Census data will be released. They have an average population of about 400. They are built from whole Mesh Blocks and there are approximately 55,000 covering the whole of Australia.
STATISTICAL AREAS LEVEL 2 (SA2s)
SA2s have an average population of about 10,000, with a minimum population of 3,000 and a maximum population of 25,000. The SA2s are the regions for which the majority of ABS sub-state intercensal data, for example Estimated Resident Populations, will be released. There are about 2,200 SA2s, built from whole SA1s.
STATISTICAL AREAS LEVEL 3 (SA3s)
SA3s are a medium sized region with a population of 30,000 to 130,000. They represent the functional areas of regional cities and large urban transport and service hubs. They are built from whole SA2s.
STATISTICAL AREAS LEVEL 4 (SA4s)
SA4s will be used for the release of Labour Force Statistics. They are built from whole SA3s.
STATE SUBURBS (SSC)
Like postal areas, State Suburbs (SSC) are not an ASGS standard geography. SSCs are based upon one or more SA1s in an attempt to match suburbs (at the time of the Census). However, unlike postal areas, State Suburbs do not cover all of Australia, although most of the population is covered. Because some surveys are based on suburb information rather than standard geographies, SEIFA is available for State Suburbs. (Note that SLAs in Brisbane and other major urban areas in Queensland, Darwin and Canberra are aligned closely with suburbs.) However, SEIFA users need to be aware that State Suburbs and suburbs are not always good matches, and should use SSCs with caution. For example, a SA1 can be matched to a single State Suburb even if that SA1 spans two suburbs. Not all SA1s are matched to State Suburbs, and these SA1s are given an Unclassified SSC code. SSCs have both a code and a name. SSC names are based on the most recent gazetted locality boundaries current at the time of a Census. There are 8529 SSCs in the 2011 ASGS, however not all of these are included in SEIFA.
STATE ELECTORAL DIVISION (SED)
A State Electoral Division is an area legally prescribed for the purpose of returning one or more members to the state or territory lower houses of parliament. Queensland has only one house of parliament at the state level, with each member representing an electoral district. State Electoral Divisions are approximated by aggregating the data for Statistical Areas Level 1 (SA1s) that best fit the area.
STANDARD GEOGRAPHIC AREAS
The Australian Statistical Geography Standard (ASGS) is the new geographical standard developed by the ABS for the collection and dissemination of statistics. It is a hierarchically structured classification with a number of spatial units to satisfy different statistical purposes.
The ASGS areas used for SEIFA 2011 are:
- Statistical Area Level 1 (SA1)
- Statistical Area Level 2 (SA2)
- Statistical Area Level 3 (SA3)
- Statisitical Area Level 4 (SA4)
- State/Territory (STE)
The following are non-ABS structures. These structures contain regions that the ABS does not define or maintain. However, SEIFA 2011 output will be produced at these levels (be it index scores, SA1 distribution data cubes or SA1 decile distribution data cubes).
- Local Government Area (LGA)
- Postal Areas (POA)
- Commonwealth Electoral Division (CED)
- State Electoral Division (SED)
- State Suburb (SSC)
Using technical language, the standard distribution chosen for SEIFA has a "mean of 1000 and a standard deviation of 100". First, all of the SA1s are ordered from the lowest to highest score. Second, all the scores are 'shifted' together so that the average area now has a new score of 1000. The areas are still in the same order, but they all have new scores spread around the average of 1000. The final stage changes how these scores are spread around the average. While they still remain in order, the scores are spread out (or condensed) so that two-thirds of the areas have 'standardised' scores somewhere between 900 and 1100; that is, approximately two-thirds of the scores lie within 100 either side of the average of 1000. This means that approximately 15% of SA1s have a score lower than 900 with the remaining 85% of SA1s having a score higher than 900. Approximately 85% of SA1s have a score lower than 1100 with the remaining 15% of SA1s have a score higher than 1100.
It is important to note that the distributions of the SEIFA indexes are not exactly a normal distribution, even though they have been standardised. However, the above proportions are roughly the same. Standardisation is useful when interpreting the scores and where the scores are used for more technical analysis.