The Australian Bureau of Statistics (ABS) recently released a new round of data based on the 2011 census. One part of this latest release that has caught the eye of the media is the 2011 Socio-Economic Indexes for Areas (SEIFA). This ranks and summarises aspects of the socio-economic conditions of people living in certain areas - such as in the electorates of our political leaders.
SEIFA data reveals that only 0.3% of opposition leader Tony Abbott’s Sydney suburban electorate of Warringah live in the most disadvantaged 10% of neighbourhoods in Australia. By contrast, in prime minister Julia Gillard’s Melbourne suburban electorate of Lalor, 8% of people live in the most disadvantaged 10% of neighbourhoods.
This tells us very little about the respective policy views of the prime minister and the opposition leader, or of their commitment to improving the lives of their constituents. But this comparison tells us a little bit about the priorities of the people who they represent.
Using the data
The four indices used to create SEIFA are the indices of Relative Socio-Economic Disadvantage (IRSD), Relative Socio-Economic Advantage and Disadvantage (IRSAD), Economic Resources (IER) and Education and Occupation (IEO).
SEIFA has a number of important policy and research purposes. Most obviously, the indices can be used for the targeting and planning of government and commercial services.
If, for example, the federal government was planning a randomised controlled trial for a new service targeted at reducing unemployment and associated disadvantage, then it is unlikely to be delivered in the relatively advantaged areas in the right hand column in the following table, but rather in the relatively disadvantaged ones on the left.
These rankings are not always surprising, but SEIFA adds some solid statistical evidence to decision making.
Another use of SEIFA is to help explain individual behaviour. For example, 27% of children in the Longitudinal Study of Australian Children who lived in the most disadvantaged 20% of neighbourhoods, when aged four to five years old, had their academic skills rated by their teachers as below average or far below average when they were six to seven years old.
For those in the most advantaged 20% of neighbourhoods, it was only 15%. We can’t say whether these differences are causal or not but we can say that living in a disadvantaged area is predictive of low outcomes.
Matters of simple curiosity can also be answered by SEIFA. It enables individuals to ask: where does my neighbourhood, suburb or town fit in compared to other parts of the country?
SEIFA also helps our understanding of the geographic context in which particular population sub-groups live. By definition, roughly 10% of all Australians live in the most disadvantaged 10% of neighbourhoods and roughly 10% live in the most advantaged 10%.
This is not, however, true of all population sub-groups. A massive 37% of indigenous Australians live in the most disadvantaged decile compared to only 2% who live in the most advantaged decile.
Not only are indigenous Australians relatively disadvantaged themselves, they live in areas where their neighbours and friends are disadvantaged. As it is these neighbours and friends that people often use to obtain labour market, education and financial support, then it is quite possible that this area-level disadvantage contributes to individual disadvantage.
Unlike in many other countries, there does not appear to be significant area-level disadvantage amongst Australian migrants. Those who arrived in Australia relatively recently (that is, between 2007 and 2011) are much less likely to live in relatively disadvantaged neighbourhoods and much more likely to live in relatively advantaged ones.
These results say a lot about the way Australia has been able to pursue a targeted migration policy that brings in highly skilled workers who are successful relatively quickly in the Australian labour market.
Limits of SEIFA
There are a number of limitations of SEIFA. It is not possible to look at the range of disadvantage for population subgroups included in the construction of the index (for example, single parents), not all input variables are unequivocally good indicators of advantage or disadvantage.
For instance, the percentage of dwellings with three or more cars might be an indicator of relatively poor public transport options in the area. The construction of SEIFA is therefore limited by what data is available on the census.
SEIFA also tells us very little about the causes of disadvantage, either at the individual or area-level. In terms of knowing how to alleviate disadvantage, what continues to be required is good quality longitudinal data and policy evaluations.
These limitations notwithstanding, SEIFA represents an important contribution to evidence-based policy making in Australia and has the potential to support research into some of Australia’s major policy and social issues - if applied correctly, with its relative strengths and limitations at the forefront of any policy discussion.