A major theme of Dropping off the Edge 2015 is the consistency with which localities identified as extremely disadvantaged in 2015 resemble those similarly ranked in earlier studies. This is especially true of the localities comprising the two top ‘bands’ (12 most disadvantaged places) derived by a statistical tool that captures what the indicators share in common.
For example, in New South Wales, nine of the top 12 ‘most disadvantaged’ postcodes in 1999 remain in the top 12 in 2015. Further examples can be found in each state and territory chapter.
The second way of gaining an overall picture of disadvantage is to simply count the number of times each location fills one of the highest ranking spots on each of the 22 indicators. Generally speaking, a ranking in the top 5% of results is considered a ‘high’ ranking. The two methods produced similar results but with some variations reflecting diverse political, demographic, economic and social landscapes across the different jurisdictions. Nevertheless, the data permits some significant messages to be read on a jurisdiction by jurisdiction basis.
In every jurisdiction there is a marked degree of spatial concentration of disadvantage.
- In Queensland, 6% of statistical local areas (SLAs) accounted for roughly half of all ‘top’ ranks
- In South Australia, 5.6% of SLAs accounted for 57% of ‘top’ ranks
- In NSW, Victoria and Western Australia, 1.5% of postcodes accounted for 12-14% of ‘top’ ranks
- In Tasmania, the five most disadvantaged local government areas (out of 29) accounted for 64% of the ‘top’ ranks
- In Northern Territory, 6% of the SLAs accounted for 50% of ‘top’ ranks.
In each jurisdiction, the profiles of localities regarded at ‘most’ or ‘next most’ disadvantaged, were examined to discern whether there were recurring characteristics. Some variations were found, such as the relative importance of rent assistance in Victoria and this indicator’s virtual absence in New South Wales.
However, the latter state’s profile serves as a useful template for identifying core characteristics of Australia’s disadvantaged communities. In two-thirds of those localities in New South Wales criminal convictions were a dominant characteristic, and adult imprisonment and juvenile offending were at significantly high rates within communities additionally burdened by long and short unemployment, disabilities, lack of formal qualifications, deficient education generally, low family incomes, domestic violence and mental health problems. With one exception, criminal justice indicators were also prominent in the profile of Victoria’s disadvantaged areas, the exception being the lower frequency with which juvenile offending was to the fore.
Young adults, no full-time work or education/training, were also less prominent. The overall level of education and deficiencies with respect to post-school qualifications were elements of the Victorian profile but NAPLAN results were less of a distinguishing characteristic.
In South Australia unemployment, overall level of education, criminal convictions and unengaged young adults were the prominent features, a pattern similar to that of Queensland, South Australia, and Northern Territory and with a small number of LGAs involved, Tasmania. The high frequency indicators in Western Australia’s disadvantaged areas placed more emphasis on NAPLAN deficiencies, internet access, unengaged young adults, overall education, prison and psychiatric admissions.
The concentration of disadvantage can be illustrated clearly when we compare the rate of occurrence of various indicators within the 3% most disadvantaged localities versus the remaining 97% in each jurisdiction. These comparisons serve to underline the human significance and opportunity-stunting consequences of the statistical patterns revealed by the research. Their main features are summarised in each state overview. However, a few examples at this point should serve to illustrate the social and individual significance of the comparisons.
Normally we would regard a doubling of the rate of an occurrence as being a matter of note. That is what we find to be the case with criminal convictions in all states except Tasmania. However, in the case of juvenile offending in Victoria, a State with an acknowledged overall modest rate, the ratio favouring the general community was almost three-and-a-half times less than the 3% most disadvantaged group. These differences were by no means extreme in comparison with some of the other jurisdictions. For example, in Western Australia, the proportion of prison admissions was eight times greater in the top 3% localities, and approximately 5-6 times higher with respect to both unemployment indicators, and also young people not engaged in work or study and low overall level of education.
This information actually offers some reassurance to governments and finance controllers: to concentrate on the most cumulatively disadvantaged localities throughout Australia is not to ‘open Pandora’s box’ but offers an opportunity to commit to a manageable number of highly disadvantaged communities.