Drivers Of Persistent Disadvantage

Mapping the Potential examines persistent disadvantage using four drivers: education, health, economic and social factors. Each driver includes a range of loaded variables.  

Persistent disadvantage involves more than economic factors. It is a holistic framing of indicators which include economic, education, health and social factors, all of which contribute to a community’s experience of persistent disadvantage.

Economic

The Economic driver relates to relative disadvantage in monetary and economic opportunity terms, this often is experienced as low income or welfare. 

Education

The Education driver tells us about disadvantage in terms of development for school-aged children; disadvantage that might be experienced through illiteracy, or early school leaving.  Often this driver is equated with future unemployment.The Economic driver relates to relative disadvantage in monetary and economic opportunity terms, this often is experienced as low income or welfare. 

Health

The Health driver relates to relative disadvantage in terms of physical well-being; often experienced through chronic illness and disease associated with obesity and old age.  This driver can be understood in terms of impairment.

Social

The Social driver captures disadvantage due to potential for marginalisation. Indigenous, ethnic, single parent or non-English speaking status can be key factors.

Loadings

A loading is constructed from a statistical process used to calculate the weighting of index/drivers.  Each variable is tested to see which contributes most to disadvantage. The most influential variable in each index is called the principal component.  The relative influence of all the other variables is then calculated. The results are then represented as a loading. Suburb results and the loading are combined to create index scores. This is the numerical score contained in the Report.  The lower the score the stronger the influence of the driver on disadvantage.

Economic Driver Variables

An important factor in an individual’s ability to contribute to society is through their economic contribution, often through employment, or through their circumstances which may be improved by owning a house, education or living in a more affluent area. Variables for the economic driver and corresponding loading below:

VariableLoading
Percentage of household low income in both 2011 and 2016 Longitudinal Census (equivalised income < $25,999 per year) (Census)  0.73
Percentage of persons with low income (<$25,999 per year) (Census)0.81
Percentage of families with jobless parents (Census)  0.76
Percentage of unemployment rate (Census)  0.72
Percentage of adult population high school only (Census)  0.68
Percentage of households needing extra bedroom (Census) 0.52
Percentage of households renting 0.35
Percentage of households in public housing 0.61
Percentage of households in housing stress (30/40 rule) (Census/PolicyMod) 0.88
Percentage of working age population on pensions or allowances0.90
Percentage of adult population on age pension0.60
SA2 median house price-0.53
Percentage of households needing extra bedroom 2011 and 2016 persistence (Longitudinal Census)0.54
Percentage of households in housing stress (approximate 30/40 rule) 2011 and 2016 persistence (Longitudinal Census)0.55
Percentage of families with jobless parents 2011 and 2016 persistence (Longitudinal Census )0.79
























*Low skill population, Vocational Education, Kindergarten variables not included due to low loadings.

All variables with the exception of low skilled jobs, vocational education and kindergarten were included in the economic driver principal component. No two variables had excessively high correlations with other included variables. In this driver we have included a component that relates not just to current disadvantage, but also persistent disadvantage. The persistence variables included low income households (roughly equating to relative poverty), jobless families, housing stress and the need for extra bedrooms in a dwelling.

All variables with the exception of low skilled jobs, vocational education and kindergarten were included in the economic driver principal component. No two variables had excessively high correlations with other included variables. In this driver we have included a component that relates not just to current disadvantage, but also persistent disadvantage. The persistence variables included low income households (roughly equating to relative poverty), jobless families, housing stress and the need for extra bedrooms in a dwelling.

Education Driver Variables

An important determinant for future wellbeing is a strong education, particularly in the early years of life. We developed an education disadvantage index based on measures that we expect relate to education disadvantage (such as high school only education) and more direct measures (such as language and learning issues). The list of variables below is highly weighted towards educational problems for children. See p 43 of the Report for further details.

VARIABLELOADING
Percentage of adult population high school education only (Census)  0.64
Percentage of children with health problems 2009^ to 2018 average  0.92
Percentage of children with social development issues 2009^ to 2018 average  0.93
Percentage of children with language issues 2009^ to 2018 average 0.92
Percentage of children with communication issues 2009^ to 2018 average 0.90
Percentage of children with emotional issues 2009^ to 2018 average 0.89

^ For some SA2s not all years from 2009 were available. *Vocational Education population share, kindergarten attendance, and all educational developmental variables at a point in time (2016) not included due to low loadings or higher correlations with other variables. 

All variables included in the PCA were included in the final model and the level of correlation between variables was not too high to exclude any variables.

Health Driver Variables

Our health disadvantage index combines a range of variables that were considered to relate to either direct or indirect health problems for people.  Variables such as smoking and alcohol expenditure are variables that for an individual at a given point in time may not be presently impacting their health.  However, they are variables that may have an association at a regional level with health problems.

Health disadvantage variables (Only loadings > 0.3 included)*

VARIABLELOADING
Percentage of population with a disability (AIHW) 0.57
Average spend on tobacco per week (Regional  Policymod – synthetic SA2 data combining the ABS Household Expenditure Survey and the ABS Census) 0.44
Percentage of population with Type 2 diabetes (PHIDU, Torrens University)0.86
Percentage of population with mental health problems (PHIDU, Torrens University) 0.68
Percentage of population with mood disorders (PHIDU, Torrens University)0.68
Percentage of population with circulatory problems (PHIDU, Torrens University)0.80
Percentage of population with heart problems (PHIDU, Torrens University)0.83
Percentage of population with respiratory problems (PHIDU, Torrens University) 0.52
Percentage of population with asthma problems (PHIDU, Torrens University) 0.71
Percentage of population with pulmonary problems (PHIDU, Torrens University)0.93
Percentage  of population with arthritis (PHIDU, Torrens University) 0.83
Percentage of population with obesity (PHIDU, Torrens University) 0.74
Percentage of population with disability in 2011 and 2016 ABS longitudinal Census 0.51

*Alcohol expenditure, aged care population and musculoskeletal problems variables not included to low loading or too high correlations with other variables.

We found that 13 of the original 16 variables satisfied the criteria that loadings (correlations with the first principal component) should be greater than 0.3 and that no two variables should have correlations greater than 85 per cent. Our health index, does not contain all dimensions or aspects of health and in an ideal world we would have a broader coverage of health conditions or health-related risk factors. It is intended that our future analysis will incorporate additional health variables at the SA2 (suburb) level that were not accessible at the time of producing this report.

Social Disadvantage Driver Variables

Socially disadvantaged individuals are those who have been subjected to racial/ethnic prejudice or, cultural bias. Quantitative data is not always directly available from our data sources, but it is possible to use variables that could reasonably be expected to proxy for groups more likely to be exposed to such disadvantage.  We use Census data to estimate regional variables on Indigenous status, whether English is spoken at home, being a single parent, access to the internet, country of birth and volunteering. It is not the case that any or all of these variables imply that a person is socially disadvantaged but they are variables associated with a limited connectedness while single parent status is often associated with a range of financial, time and social pressures.

VARIABLELOADING
Percentage of adults with poor English*
Percentage of adults volunteering* 
Percentage of adults born overseas*
Percentage of household single parents 0.75
Percentage of persons Indigenous0.78
Percentage of dwellings no internet connection 0.86

*Volunteering, poor English, born overseas were not included due to negative correlations with the principal component index.

In the SEIFA Index the ABS also removes variables that are very strongly related to the principal component. The correlation between internet connection and the overall Index is relatively high. However, we have retained this variable. Three variables were dropped due to negative loadings. As we are constructing an index of disadvantage we only retain variables with a positive association with disadvantage. Ideally, a greater range of variables would be included in this PCA.

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