Using and interpreting SEIFA

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Socio-Economic Indexes for Areas (SEIFA): Technical Paper
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This chapter provides information to assist in the appropriate use of SEIFA and to help users gain the most value from the product.

Broad guidelines on appropriate use

Area level indexes

The indexes are assigned to areas, not to individuals. They indicate the collective socio-economic characteristics of the people living in an area. A relatively disadvantaged area is likely to have a high proportion of relatively disadvantaged people. However, such an area is also likely to contain some people who are relatively advantaged. When area level indexes are used as proxy measures of individual level socio-economic advantage and disadvantage, many people are likely to be misclassified. This is known as the ecological fallacy. Wise and Mathews (2011) conducted an investigation into the extent of this issue as it relates to SEIFA.

Ordinal indexes

As measures of socio-economic level, the indexes are best interpreted as ordinal measures. They can be used to rank areas and are also useful to understand the distribution of socio-economic conditions across different areas. Also, the index scores are on an arbitrary numerical scale. The scores do not represent some quantity of advantage or disadvantage. For example, we cannot infer that an area with an index value of 1000 is twice as advantaged as an area with an index value of 500.

For ease of interpretation, we generally recommend using the index rankings and quantiles (e.g. deciles) for analysis, rather than using the index scores. Index scores are still provided in the output and can still be used for analysis when appropriate. For more information on index scores, rankings, and quantiles, refer to basic output: scores, ranks, deciles and percentiles.

Importance of the underlying variables

Each index is constructed using a weighted combination of selected variables. The indexes are 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 each index, 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 in each index. This will allow them to make an informed decision on whether an index is appropriate for their particular purpose. Choice of index provides some tips on choosing which of the four indexes to use.

Choice of index

Depending on the aim or context of the analysis, one of the SEIFA indexes may be more appropriate than the others. Below are some aspects to be considered.

  • The concept and variables underlying each index. The concepts behind each index are described in defining the concept behind each of the four indexes. The final variable lists for each index are in the technical details of each index: variables and loadings.
  • The degree to which the four indexes are correlated with each other – this is discussed in relationships between the indexes.
  • The IRSD ranks areas on a continuum from most disadvantaged to least disadvantaged, while the other three indexes (IRSAD, IER, IEO) rank areas on a continuum from most disadvantaged/least advantaged to most advantaged/least disadvantaged.
  • The IRSD and IRSAD are more general measures in the sense that they summarise variables from a wider range of socio-economic dimensions. The IER and IEO are more targeted measures aimed at capturing narrower concepts.
  • Simpler measures, such as income or employment status, may be more appropriate than SEIFA for some analysis. For an in-depth discussion on choosing a socio-economic measure, refer to Information Paper: Measures of Socioeconomic Status, New Issue for June 2011.

Using index scores for areas larger than SA1

Given that the indexes are area level measures, they have the tendency to mask some underlying diversity. In some applications of the indexes, it may be important to identify diversity of socioeconomic characteristics within areas.

When using an index at a geographic level higher than SA1 (e.g. SA2s and LGAs), we do have some scope to assess the diversity within that area by looking at its constituent SA1s. There is further discussion about assessing diversity within areas in Wise and Mathews (2011) and Radisich and Wise (2012). The second paper also proposes an additional measure that can be used to identify diverse larger areas. This measure is called the ‘SA1-concentration score’ and can identify the presence of disadvantaged SA1s within an overall advantaged large area.

To enable the analyses described above, an additional type of output has been released for SEIFA 2021. For all geographic levels higher than SA1 for which index scores are released, the corresponding SA1 distributions within those areas have been presented in spreadsheets.

As noted previously, SEIFA scores for SALs and POAs with small populations should be used with caution, because the SA1 boundaries may not correspond closely to the higher level area. For more information, refer to geographic output levels for SEIFA 2021.

Mapping the indexes

Maps of the indexes are an excellent way of observing the spatial distribution of relative socio-economic advantage and disadvantage. Refer to interactive maps for available maps of the SEIFA 2021 indexes.

Using the indexes as contextual variables in social analysis

SEIFA index ranks and deciles are commonly merged onto a person level dataset based on the area in which that person resides. The indexes can then be used to help investigate the relationship between disadvantage or advantage and other variables of interest. This type of analysis can yield some very interesting findings; however, it is important to interpret the findings correctly. Some interpretive issues are discussed below.

A SEIFA index refers to the area in which a person lives. It is a contextual variable. It is incorrect to say that a person is very disadvantaged just because they live in a very disadvantaged area. It is true that living in a very disadvantaged area may disadvantage them to a certain extent, but it is possible that they are advantaged in other respects such as having a good education and earning a high income, and are therefore not typical of other residents in that area. The issue of diversity of individuals within areas is further investigated and discussed in SEIFA: Getting a Handle on Individual Diversity Within Areas, 2011.

It is desirable to use the smallest geographic unit possible when merging an index to another dataset. In the case of SEIFA 2021, the SA1 is the smallest unit available, and if possible, SA1s should be derived on the dataset to which SEIFA scores are being appended.

Area-based quantiles versus population-based quantiles

The word ‘quantiles’ is used to collectively describe measures such as percentiles and deciles. In the spreadsheets in which the indexes are presented, quantiles (percentiles and deciles) are presented in addition to the index scores and rankings, as described in basic output: scores, ranks, deciles and percentiles. These quantiles are calculated based on dividing the number of areas into equal groups. These are called area-based quantiles.

An alternative way of defining the quantiles is to divide them into equal groups based on the number of people living in those areas. The quantiles would then contain an equal number of people (or at least as can be best achieved) in each group, rather than an equal number of areas. These are called population-based quantiles.

The ABS publishes area-based quantiles because they are easier to interpret, since SEIFA is an area-based measure. They also serve most analytical purposes. There are some instances in which the use of population-based quantiles is appropriate. Users can create their own population-based quantiles using information already available in the output spreadsheets. Population-based deciles are also available in Census TableBuilder. As mentioned above, population-based quantiles can be difficult to interpret, so users should take care in how they are applied. The population-based quantiles represent groups of individuals who live in similarly ranked areas, as opposed to groups of similarly ranked individuals.

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