SAE Manual Sections 1 to 4_1 (May 06).pdf - National Statistical ...

A Guide to Small Area Estimation - Version 1.1 05/05/2006
ultimately hinge upon user decision making requirements. It makes sense to choose
small area that are as close as possible to the areas used for program planning and
implementation. However, such areas are often really no more than administrative
regions, chosen for pragmatic or logistical reasons such as transport costs or workforce
management efficiency. Statistical units within these administrative regions are not
necessarily homogenous with respect to the variable we are trying to calculate small area
estimates for. If this is the case it may be worth considering (subject to the minimum
sample size requirement) small areas at a finer level with greater homogeneity to obtain
a better fitting model. Small area estimates at this level can then be aggregated to the
required administrative regional level.
For example, in the disability empirical study, disability programs are funded and
administered at the level of Disability and Health Services Regions (DHSR) which are
aggregations of usually a few LGAs. LGA was considered sufficiently close for modelling
purposes while also having the advantage of sufficient sample sizes and higher level of
homogeneity with respect to disability characteristics.
Another example is that of producing small area estimates of water usage. One might
consider using water catchment areas because that is the level required by users,
however these are not always standardised across water and energy authorities. There is
also the problem of geocoding ASGC classifications on which ABS data is based to the
water catchment area. Water catchment areas can also be vast along major river systems,
encompassing very different land uses, rainfall patterns and geological drainage features.
3.4 Variable of Interest
The variable of interest is typically measured from an ABS sample survey. This forms our
dependent variable to build the small area model around. If the proportion of the
population with a characteristic of interest is constant across broad geographic areas
(e.g. assuming each small area has say, the same rate of heart attacks within NSWs), then
auxiliary data are not really needed and a simple technique such as the broad area ratio
estimator will give good results.
In practice, however, this will be a strong assumption to make. If we believe that small
area proportions vary with other factors then auxiliary information will be required to
build a model. The auxiliary data can help explain the variation between small areas and
assist in creating quality small area estimates.
Another point for consideration is that in many applications there will be not just one
but a number of variables of interest requiring small area estimates. Auxiliary data may
not be available for each of these and the strength of the relationship between each
variable of interest and the available auxiliary variables may vary markedly. Prioritising
the variables of interest with users will assist in focusing effort to improve the quality of
those estimates that matter most.
3.5 Quality of Auxiliary Data
Potential auxiliary data should be evaluated for their relationship to the variable(s) of
interest, both theoretically and statistically as well as the accuracy and reliability with
which they have been collected. The theoretical relationship should emanate from
tested social or economic theories. A careful examination should be made to understand
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