Now it’s time to turn our attention to the relationship between these socio-economic indexes, and other indicators of socio-economic well being, advantage and disadvantage. We will shifting back to the SA2 level in Sydney, and combining some of our SEIFA datasets with other datasets.
This is where the fundamental value of the AURIN Workbench becomes apparent. In this analysis we are going to combine datasets from disparate domains along lines of a common geography and start to develop a much richer picture for our understanding of the lived experience of socio-economic status.
We are going to include these variables in this exercise
- The 2016 SA2 Index of Relative Socio-economic Advantage and Disadvantage (IRSAD)
- ABS Index of Household Advantage and Disadvantage – Percentage of Households (SA2) 2016
- Poverty Rate (a)
- Housing Stress (a)
- Median Household Income (a)
- Gini Coefficient (a)
- Youth Engagement In Work/Study Fully Engaged % (b)
- Labour Force Statistics Unemployment Rate % (b)
- Highest Year Of School Completed – Persons Aged 15 Years And Over Completed Year 10 Or Equivalent % (b)
- Housing Suitability Dwellings With Extra Bedrooms Needed No. (c)
- Selected Government Pensions & Allowances Newstart Allowance No. (d)
- Selected Government Pensions & Allowances Youth Allowance (Full Time Students/Apprentices) No. (d)
- Selected Government Pensions & Allowances Age Pension – Centrelink No. (d)
- Selected Government Pensions & Allowances Disability Support Pension No. (d)
- Total Dwelling Count 2016 (e)
- Total Usual Resident Population 2016 (e)
The variables marked (a) can be found in the NATSEM – Social and Economic Indicators Synthetic Estimates SA2 2016 dataset.The variables marked (b) can be found in the ABS – Data by Region – Education & Employment (SA2) 2011-2017 dataset. The variables marked (c) can be found in the ABS – Data by Region – Family & Community (SA2) 2011-2016 dataset. The variables marked (d) can be found in the ABS – Data by Region – Income (Including Government Allowances) (SA2) 2011-2017 dataset, while the variables marked (e) can be found in the SA2 Aggregated Population & Dwelling Counts 2016 Census for Australia dataset.
Please note that there is a little bit of data management before we launch into mapping and exploring these variables. Just remember that data processing management is an important part of any analysis, so you should become familiar with it!
Firstly, we need to convert some of our counts to proportions, to standardise them for population or dwelling counts. These are the counts marked (c) and (d) above, and they need to be standardised against the counts in (e).
Exercise 4.1 First we need to merge our datasets.
(1) Merge ABS – Data by Region – Family & Community (SA2) 2011-2016 with SA2 Aggregated Population & Dwelling Counts 2016 Census for Australia
Parameters for this are shown below
(2) Merge ABS – Data by Region – Income (Including Government Allowances) (SA2) 2011-2017 with SA2 Aggregated Population & Dwelling Counts 2016 Census for Australia.
Remember to rename your output datasets!
Next, we need to run the Generate tool to calculate proportions
For the output of (1) above, divide the Housing Suitability Dwellings With Extra Bedrooms Needed No. variable (c) by the Total Dwelling Count (2016)
Parameters for this are shown below
For the output of (2) above, divide each of the number of different government pensions and allowances by the Total Usual Resident Population (2016) variable. You will need to do this sequentially for each of the variables, so remember to carefully name each output column and dataset!
Merge Parameters

Generate Parameters

Your final output tables from these tools should look something like the images below


We are now ready to merge all of our datasets together into one large dataset. This will allow us to undertaken correlation analyses, or other statistical analyses.
Exercise 4.2: Sequentially merge your SA2 Datasets together – Remember . you already merged all of your SEIFA datasets together in our first exercise, so you don’t need to redo that step. Also, you don’t need to add the Merge ABS – Data by Region – Income (Including Government Allowances) (SA2) 2011-2017 or the Merge ABS – Data by Region – Family & Community (SA2) 2011-2016, or the SA2 Aggregated Population & Dwelling Counts 2016 Census for Australia because you have created new datasets from these – just add your output datasets from above!
We have named our final dataset:
IER + IEO + IRSD + IRSAD + IHAD + NATSEM + Inc/Emp + Crowded + Prop_Pensions
Once you have created this dataset, you can delete the intermediate datasets to keep your data panel as uncluttered as possible – but make sure you have included all of the variables!
Now we are ready to do some mapping!
Exercise 4.3: Create some choropleth and choropleth centroid maps of the various variables in the master dataset you have just created.
Be creative and insightful – what potential relationships might you pick up?
We have created four here, but there are 210 possible combinations of maps!
SEIFA IRSD Index Score vs. Proportion of Total Dwellings that are Crowded

SEIFA IRSAD Index Score vs. Gini Co-efficient (Income Inequality)

SEIFA IEO vs Percentage of 15-24 Year Olds Engaged in Work or Study

SEIFA IER vs Proportion of Population Receiving New Start Allowance

Now we are going to run a correlation analysis on all of our variables – the output is going to be large so it will require some careful, sensible management!
Exercise 4.4: Run a correlation analysis on the merged dataset. The parameters for this are shown below
CORRELATION PARAMETERS

The outputs of the correlation analysis are shown in the table below. The bottom left contain the correlation r values, while the top right contain the significance (green = statistically significant, red = statistically non-significant)
| | | | | | | | | | | | | | | | | | | | |
| IER Score | IEO Score | IRSD Score | IRSAD Score | IHAD Q1 % | IHAD Q2 % | IHAD Q3 % | IHAD Q4 % | Gini Co-efficient | % Housing Stress (30/40) | Median Income | Poverty Rate | % Youth Earn/Learn | % Unemployment | % Yr 10 Leaving | % Crowded Dwellings | % New Start | % Youth Allowance | % Age Pension | % Disability Pension |
IER Score | 1/NaN | 0 | 0 | 0 | 0 | 0 | 0.0033 | 0 | 0.1224 | 0 | 0 | 0 | 0 | 0 | 0.0002 | 0 | 0 | 0 | 0.0218 | 0 |
IEO Score | 0.4019 | 1/NaN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
IRSD Score | 0.7766 | 0.8435 | 1/NaN | 0 | 0 | 0 | 0 | 0 | 0.0245 | 0 | 0 | 0 | 0 | 0 | 0.0013 | 0 | 0 | 0 | 0 | 0 |
IRSAD Score | 0.6605 | 0.9419 | 0.9598 | 1/NaN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
IHAD Q1 % | -0.7899 | -0.7298 | -0.9059 | -0.8837 | 1/NaN | 0 | 0 | 0 | 0.1839 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
IHAD Q2 % | -0.5017 | -0.7974 | -0.6962 | -0.8118 | 0.6647 | 1/NaN | 0.0247 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
IHAD Q3 % | 0.1755 | 0.3857 | 0.4447 | 0.4305 | -0.5922 | -0.1345 | 1/NaN | 0.0002 | 0.7169 | 0 | 0 | 0 | 0.1681 | 0 | 0.0069 | 0.5386 | 0 | 0.0002 | 0 | 0 |
IHAD Q4 % | 0.7842 | 0.7842 | 0.8699 | 0.9013 | -0.8883 | -0.8887 | 0.2224 | 1/NaN | 0.0001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Gini Co-efficient | -0.0927 | 0.4631 | 0.1346 | 0.307 | -0.0798 | -0.4213 | -0.0218 | 0.2361 | 1/NaN | 0.9728 | 0 | 0.3257 | 0 | 0.5401 | 0 | 0.0001 | 0.0023 | 0.0524 | 0.0005 | 0.0004 |
% Housing Stress (30/40) | -0.8058 | -0.593 | -0.8609 | -0.7547 | 0.7142 | 0.4887 | -0.2639 | -0.6896 | 0.002 | 1/NaN | 0 | 0 | 0 | 0 | 0.0138 | 0 | 0 | 0 | 0.6069 | 0 |
Median Income | 0.7763 | 0.7483 | 0.8449 | 0.8792 | -0.8342 | -0.813 | 0.2429 | 0.9203 | 0.2962 | -0.7614 | 1/NaN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Poverty Rate | -0.7503 | -0.6616 | -0.9055 | -0.8034 | 0.7621 | 0.4986 | -0.3654 | -0.7006 | 0.059 | 0.9609 | -0.7405 | 1/NaN | 0 | 0 | 0.2816 | 0 | 0 | 0 | 0.0459 | 0 |
% Youth Earn/Learn | 0.5197 | 0.5371 | 0.5529 | 0.6071 | -0.6089 | -0.6545 | 0.0828 | 0.7251 | 0.3172 | -0.3642 | 0.6134 | -0.3559 | 1/NaN | 0 | 0 | 0.2166 | 0 | 0.6095 | 0 | 0 |
% Unemployment | -0.7778 | -0.6605 | -0.9025 | -0.8153 | 0.7641 | 0.4999 | -0.4 | -0.6921 | 0.0368 | 0.8844 | -0.7454 | 0.9082 | -0.358 | 1/NaN | 0.3202 | 0 | 0 | 0 | 0.005 | 0 |
% Yr 10 Leaving | 0.2181 | -0.6251 | -0.1913 | -0.4363 | 0.2504 | 0.5839 | -0.1615 | -0.3806 | -0.6029 | -0.1473 | -0.3116 | -0.0647 | -0.409 | -0.0597 | 1/NaN | 0 | 0.0001 | 0 | 0 | 0 |
% Crowded Dwellings | -0.7259 | -0.3462 | -0.6657 | -0.5088 | 0.4728 | 0.2516 | -0.037 | -0.4715 | 0.227 | 0.8196 | -0.5092 | 0.7936 | -0.0742 | 0.7632 | -0.4138 | 1/NaN | 0 | 0 | 0.0042 | 0 |
% New Start | -0.7086 | -0.787 | -0.9247 | -0.8994 | 0.8507 | 0.6718 | -0.4305 | -0.8201 | -0.1819 | 0.7606 | -0.789 | 0.7966 | -0.627 | 0.8205 | 0.2355 | 0.5113 | 1/NaN | 0 | 0 | 0 |
% Youth Allowance | -0.5487 | -0.3788 | -0.623 | -0.4956 | 0.452 | 0.2457 | -0.2183 | -0.396 | 0.1163 | 0.7178 | -0.4642 | 0.7372 | -0.0307 | 0.709 | -0.2772 | 0.6852 | 0.5574 | 1/NaN | 0.3502 | 0 |
% Age Pension | -0.1373 | -0.4507 | -0.3392 | -0.4658 | 0.5573 | 0.4984 | -0.5224 | -0.4847 | -0.2057 | 0.0309 | -0.4821 | 0.1196 | -0.3072 | 0.1677 | 0.5727 | -0.1708 | 0.3626 | -0.0561 | 1/NaN | 0 |
% Disability Pension | -0.6524 | -0.7153 | -0.8172 | -0.8288 | 0.8582 | 0.6433 | -0.4754 | -0.8011 | -0.2098 | 0.6012 | -0.7594 | 0.6474 | -0.7213 | 0.6696 | 0.3508 | 0.2702 | 0.8919 | 0.3625 | 0.5126 | 1/NaN |
| | | | | | | | | | | | | | | | | | | | |
This table provides a large amount of information about the lived experience of socio-economic disadvantage. Some important take home relationships:
- All of the SEIFA Indexes are significantly negatively associated with housing stress, poverty rates, crowded dwellings, unemployment rates and the proportion of the population receiving the government pensions
- All of the SEIFA indexes are significantly positively associated income levels, percentage of young people earning or learning
- Housing stress and crowded dwellings are significantly positively associated
- Income inequality (Gini coefficient) increases with increasing advantage for all of the SEIFA indexes except the Index of Economic Resources.
- IHAD Quartile 1 proportions were strongly positively associated with housing stress, unemployment, crowded dwellings, and the proportion of the population on government allowances or pensions.
What are relationship can you detect in this table? Are they positive or negative? Strong or weak? Statistically significant?