Socio-Economic Indexes for Areas (SEIFA), Australia

Latest release

Ranks areas according to their relative socio-economic advantage and disadvantage using Census data.

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Reference period
2021

Key statistics

According to SEIFA 2021:

  • Woollahra on Sydney Harbour’s south shore is the most advantaged Local Government Area (LGA) in Australia
  • of the ten highest scoring LGAs in Australia, six are in Sydney, three in Perth, and one in Darwin
  • Woorabinda, 170km southwest of Rockhampton in Central Queensland, is the most disadvantaged LGA in Australia
  • of the ten lowest scoring LGAs, six are in Queensland and four are in the Northern Territory
  • generally, disadvantaged areas tend to be in regional and remote communities, while advantaged areas tend to be in major cities.
Ten most advantaged LGAs

Rank

LGA

Population

State

1

Woollahra

53,496

New South Wales

2

Mosman

28,329

New South Wales

3

Ku-ring-gai

124,076

New South Wales

4

Darwin Waterfront Precinct

293

Northern Territory

5

North Sydney

68,950

New South Wales

6

Waverley

68,605

New South Wales

7

Lane Cove

39,438

New South Wales

8

Peppermint Grove

1,597

Western Australia

9

Nedlands

22,132

Western Australia

10

Cottesloe

7,970

Western Australia

a. Census 2021 Usual Resident Population

Ten most disadvantaged LGAs

Rank

LGA

Population

State

1

Woorabinda

1,019

Queensland

2

Cherbourg

1,194

Queensland

3

Belyuen

149

Northern Territory

4

West Daly

2,973

Northern Territory

5

Yarrabah

2,505

Queensland

6

Kowanyama

1,079

Queensland

7

Wujal Wujal

276

Queensland

8

East Arnhem

8,778

Northern Territory

9

Doomadgee

1,387

Queensland

10

Central Desert

3,591

Northern Territory

a. Census 2021 Usual Resident Population

SEIFA – Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) Quintiles for all LGAs

Map of SEIFA Index of Relative Socio-economic Advantage and Disadvantage LGA quintiles for Australia

A map of all LGAs in Australia. Every LGA is coloured in one of 5 colours (red, orange, yellow, light blue, and dark blue). Each colour represents a different quintile score from the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD). LGAs with a score of 1, are coloured red and represent the most disadvantaged LGAs in Australia, LGAs with a score of 2 are coloured orange and are less disadvantaged than those with a score of 1, those with a score of 3 are coloured yellow and represent LGAs less disadvantaged than those with a score of 2, and so on up through a score of 4 coloured light blue, and a score of 5 coloured dark blue representing the most advantaged LGAs.

SEIFA 2021 includes data cubes for a range of geographies. It links to Methodology, a Technical Paper discussing how SEIFA is created, and a Media Release. Interactive maps will be added on 9 May 2023. SEIFA data is also available in TableBuilder.

Overview

The 2021 Census of population and housing (Census) provides information on a range of social and economic characteristics of Australia’s population. People using Census data are often interested in a summary measure of Census data, rather than looking at individual characteristics. SEIFA is one of the commonly used summary measures.

SEIFA combines Census data such as income, education, employment, occupation, housing and family structure to summarise the socio-economic characteristics of an area.

Each area receives a SEIFA score indicating how relatively advantaged or disadvantaged that area is compared with other areas.

Four SEIFA indexes

SEIFA is a collection of four indexes, each summarising a different aspect of the socio-economic conditions in an area using different Census data:

  • the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) focuses on both advantage and disadvantage
  • the Index of Relative Socio-economic Disadvantage (IRSD) focuses on relative socio-economic disadvantage
  • the Index of Education and Occupation (IEO) focuses on relative Education and Occupation advantage and disadvantage
  • the Index of Economic Resources (IER) focuses on Economic advantage and disadvantage.

The same area may score differently for each index due to their constituent variables.

Geography

SEIFA 2021 is published on the following geographies:

  • Statistical Area Level 1 (SA1)
  • Statistical Area Level 2 (SA2)
  • Local Government Areas (LGA)
  • Suburbs and Localities (SAL) (replacing State Suburbs (SSCs))
  • Postal Areas (POA).

Index of Relative Socio-economic Advantage and Disadvantage (IRSAD)

The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) summarises information about the economic and social conditions of people and households within an area. This index includes both relative advantage and disadvantage measures.

A low score indicates relatively greater disadvantage and a lack of advantage in general. For example, an area could have a low score if there are: many households with low incomes, or many people in unskilled occupations, AND a few households with high incomes, or few people in skilled occupations.

A high score indicates a relative lack of disadvantage and greater advantage in general. For example, an area may have a high score if there are: many households with high incomes, or many people in skilled occupations, AND few households with low incomes, or few people in unskilled occupations.

IRSAD can be used:

  • as a general measure of advantage and disadvantage
  • to understand disadvantage, and advantage
  • to offset advantage or disadvantage in their analysis.

For example, IRSAD may be applicable when the topic being analysed is likely to be affected by both advantage and disadvantage.

IRSAD is not recommended for:

  • investigating disadvantage only
  • comparing with data that is already included in IRSAD, such as areas with a high proportion of dwellings paying high levels of rent.

Index of Relative Socio-economic Disadvantage (IRSD)

The Index of Relative Socio-economic Disadvantage (IRSD) is a general socio-economic index that summarises a range of information about the economic and social conditions of people and households within an area. IRSD only includes measures of relative disadvantage.

A low score indicates relatively greater disadvantage. For example, an area could have a low score if there are: many households with low income, or many people without qualifications, and many people in low skilled occupations.

A high score indicates a relative lack of disadvantage. For example, an area may have a high score if there are: few households with low incomes, few people without qualifications, few people in low skilled occupations.

IRSD can be used:

  • to understand an area's relative disadvantage and lack of disadvantage
  • as a broad measure of disadvantage.

For example, IRSD may be applicable when a user wants to allocate funds to disadvantaged areas.

IRSD is not recommended for:

  • investigating both advantage and disadvantage
  • comparing with data that is already included in IRSD, such as areas with a high proportion of households with low incomes.

Index of Education and Occupation (IEO)

The Index of Education and Occupation (IEO) reflects the educational and occupational level of communities. The education variables reflect educational attainment or if further education is being undertaken. The occupation variables are based on the Australian and New Zealand Standard Classification of Occupations (ANZSCO). It classifies the workforce into groups of occupations, skill levels and employment status. Unlike the other indexes IEO does not include any income variables.

A low IEO score indicates relatively lower education and occupation levels of people in the area. For example, an area could have a low score if there are: many people without qualifications, or many people in low skilled occupations, or many people unemployed, AND few people with a high level of qualifications or in highly skilled occupations.

A high score indicates relatively higher education and occupation status of people in the area in general. For example, an area could have a high score if there are: many people with higher education qualifications or many people in highly skilled occupations, AND few people without qualifications or few people in low skilled occupations.

IEO can be used:

  • to understand education and occupation variables
  • to understand the relationship between income, education, and employment.

 

IEO is not recommended:

  • for investigating disadvantage only
  • as a broader measure of advantage and disadvantage
  • for comparing with data that is already included in IEO, such as unemployment.

Index of Economic Resources (IER)

The Index of Economic Resources (IER) focuses on the financial aspects of relative socio-economic advantage and disadvantage, by summarising variables related to income and housing. IER excludes education and occupation variables as they are not direct measures of economic resources. It also excludes assets such as savings or equities which, although relevant, cannot be included as they are not collected in the Census.

A low score indicates a relative lack of access to economic resources. For example, an area may have a low score if there are: many households with low incomes, or many households paying low rent, AND few households with high income, or few people who own their home.

A high score indicates relatively greater access to economic resources. For example, an area may have a high score if there are: many households with high income, or many people who own their home, AND few low-income households, or few households paying low rent.

IER is recommended for understanding an area's access to economic resources (e.g. understanding housing as well as income).

IER is not recommended:

  • for investigating disadvantage only as this index measures both advantage and disadvantage
  • as a general measure of advantage and disadvantage
  • for comparing with data that is already included in IER, such as household income.

Interactive maps

SEIFA data can be viewed on an interactive map.

The maps display quintiles for the four SEIFA indexes.

Quintiles divide a distribution into five equal groups. The lowest scoring 20 per cent of areas are given a quintile number of one, the second-lowest 20 per cent are given a quintile number of two and so on, up to the highest 20 per cent of areas which are given a quintile number of 5.

The quintiles are area-based. This means that each quintile contains an equal number of areas. They may not contain an equal number of people or dwellings.

Areas without SEIFA scores are coloured grey. For more information refer to Methodology.

SEIFA data is available as a web service. For more information, see Data downloads.

How to use

Geographic areas

Data downloads

There are four types of data cubes: Indexes, Population Distributions, SA1 Distributions, and Standardised Variable Proportions.

Download all (31.61 MB)

Index data cubes

These data cubes contain area-based SEIFA scores for each of the four indexes. In addition to the index score, the area’s rank, corresponding decile, and percentile are provided at national and state/territory level.

The SA1 level indexes are the foundation from which all the data cubes are derived. Larger geographic areas are calculated by taking population-weighted averages of SA1 scores.

Data files
Download all (1.6 MB)

Population distribution data cubes

These data cubes can be used to understand the diversity of the SA1 SEIFA scores within larger areas. They contain tables and graphs showing the percentage of the population living in SA1s with scores in particular ranges.

If an area has a diverse range of SA1 scores, then the population will be spread widely across the scores. An area with a low level of diversity will have most of the population falling within a small range of scores. Index scores are not calculated for individuals, but for areas. The tables and charts reflect the range of SA1 scores making up the population within an area.

Data files
Download all (4.96 MB)

SA1 distribution data cubes

These data cubes can be used to understand the diverse range of the SA1 SEIFA scores within larger areas. They contain the number of SA1s in each decile, for each larger area.

If an area has a diverse range, then the SA1s will be spread widely across the 10 deciles. An area with a low level of diversity will have most of the SA1s falling within a small range of deciles. 

Data files

Standardised Variable Proportions data cube

This data cube contains the standardised value of each variable for each Statistical Area Level 1 (SA1) as well as the index score for each of the four indexes.

The values have been standardised to a mean of 0 and a standard deviation of 1.

These standardised values can be used to understand the relative contribution of each variable to the SA1's SEIFA score. The greater the absolute value, the greater its contribution to the SEIFA score.

Data files
Download xlsx [75.83 MB]

Data Explorer dataset

For information on Data Explorer and how it works, see the Data Explorer user guide.

Data explorer includes SEIFA data for each of the four indexes (IRSAD, IRSD, IER, and IEO) at the following geographies:

Web services

SEIFA is available as a web service for each of the four indexes (IRSAD, IRSD, IER and IEO) at the following geographies:

Post release changes

06/10/2023

  • A new Standardised Variable Proportions data cube has been added in Data Downloads.

22/08/2023

  • Fixed a typo in the LGA indexes data cube that listed an incorrect release date.

28/07/2023

  • SEIFA web services are now available. Links have been added to the Data downloads section.
  • Fixed a typo in the static map under Key statistics.
  • Previously published research on SEIFA has been linked to the Technical paper.

09/05/2023

  • The interactive maps are now available. The interactive maps section has been updated to include a link and information about using the maps.

28/04/2023

  • The table containing indicators of advantage was labelled as "IRSAD Indicators of disadvantage", and the table containing indicators of disadvantage was labelled as "IRSAD Indicators of advantage". This mislabelling has been corrected.
  • The variable SEP_DIVORCED has been updated to SEPDIVORCED to be consistent with the Technical Paper.

Technical paper

Socio-Economic Indexes for Areas (SEIFA): Technical Paper

Methodology

Socio-Economic Indexes for Areas (SEIFA), Australia methodology, 2021

Media releases