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This document was added or updated on 15/09/2020.
EXPERIMENTAL SMALL AREA HOUSEHOLD WEALTH ESTIMATES
Non-financial assets include:
A liability is established when one unit (the debtor) is obliged, under specific circumstances, to provide a payment or series of payments to another unit (the creditor). All liabilities are financial in nature, and for all financial assets held by a household there is a corresponding liability held by another party.
Liabilities are primarily the value of loans outstanding including:
Net worth is calculated as the difference between the stock of household assets and the stock of household liabilities.
The SIH collects information by personal interview from usual residents of private dwellings in urban and rural areas of Australia (excluding Very Remote areas), covering about 97% of the people living in Australia. The small area estimates created align to the same population.
Estimates have been produced for SA2, SA3 and SA4 boundaries (2016 ASGS) and LGA boundaries (2019).
To produce estimates of net worth at the small area level, a model was created using the detailed SIH data, in conjunction with the Census data, ERP, and other administrative data to produce modelled estimates for small areas. The method assumes that the relationships observed at the higher geographic level (as available in SIH) between the characteristic of interest (net worth) and known household characteristics also hold at the small area level.
The preparation of explanatory or predictor variables is a critical aspect of small area estimation. Although limited by the data that is available, there is a wide range of demographic and socio-economic variables that can be sourced from the survey, Census of Population and Housing and administrative datasets.
As the model is predicting household outcomes, household predictors were set up from household, family and person level data. Family/person level information can be summarised for each household e.g. households where the highest educational attainment is a Bachelor Degree or Higher.
Reliability of estimates
The errors associated with the modelled estimates for small areas fall into four categories: sampling error, non-sampling error, modelling error, and prediction error. The relative root mean squared error (RRMSE) provides an indication of the deviation of the modelled estimate from the true value. The RRMSE is primarily a measure of prediction error, but in its calculation it also inherits some aspects of modelling and sampling error.
As a general rule of thumb, estimates with RRMSEs less than 25% are considered reliable for most purposes, estimates with RRMSEs between 25% and 50% should be used with caution and estimates with RRMSEs greater than 50% are considered too unreliable for general use.
Bias of estimates
Analysis of the small area estimates compared to direct survey estimates reveal that the small area estimates appear to slightly overestimate mean net worth for lower net worth areas and slightly underestimate mean net worth for higher net worth areas, as shown in Graph 1.
The diagonal line in Graph 1 is representative of when the small area estimates match the direct survey estimates exactly. The purple crosses are the small area estimates. For an unbiased set of small area estimates compared to the direct survey estimates it would be expected that there would be a random distribution of purple crosses around the diagonal line. Instead, a slightly higher density of purple crosses can be seen above the diagonal for lower net worth values (compared to below the diagonal) and then as the net worth values increase there is slightly more density below the line compared to above.
Due to the apparent bias, the ABS recommends that the small area estimates should be used for comparative purposes between small areas as opposed to using the figures as exact measures of household net worth.
Graph 1 STATISTICAL AREA LEVEL 2 (SA2) AVERAGE NET WORTH, modelled estimates vs direct survey estimates(a)
Footnote(s): (a) Direct survey estimates are from the 2015-16 Survey of Income and Housing (SIH).
Areas not available
Some small area estimates are not available for publication. Areas with a large percentage of population out of scope of the SIH or with a small number of households were excluded. Additional areas deemed to have low quality estimates were also excluded; the impact is that the minimum value for each geography is higher than it would be if these areas weren’t excluded.
SA3 and SA4 errors
In the interests of minimising computational load, the error calculations for SA3s and SA4s were derived from the SA2 errors. As a result, the errors for the SA3s and SA4s will be slightly lower than if they had been calculated directly.
Confidentiality and aggregation
Estimates have been confidentialised to ensure they meet ABS requirements for confidentiality.
Because SIH population benchmarks have been used in the modelling process, the modelled estimates provided here can also be considered perturbed. Users should note that due to perturbation, the summing, or aggregation, of the modelled estimates to derive totals (e.g. a state total) will not necessarily give the same result as corresponding published totals. In these cases, the difference between the sum of modelled estimates for small areas and the published total will be small and will not impact the overall information value of the aggregate total or any individual component.
Aggregation of the modelled estimates of small areas to capital city or state/territory level is not recommended though. If you require capital city or state/territory level estimates, the appropriate source is published survey data also available from the Downloads tab or from available survey microdata products (cat. no. 6540.0).
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