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A Guide to Small Area Estimation - Version 1.1 05/05/2006 3.6 Confidentiality Protecting the confidentiality of data provided to the ABS is of utmost importance and is enshrined in the Census and Statistics Act, 1905. The risk of breaches of confidentiality need to be carefully assessed in the case of small area data releases, as such releases naturally produce a higher level of detail than is normally the case. Hence care must be taken to ensure that the potential for identifying individual persons or businesses is greatly reduced. The risk of identification is increased when: o o o The population of interest is quite rare The geographic area is very small A major part of the small area estimate can be attributed to units with unusual characteristics. (Such as in the case of doctors in remote areas or the telecommunications sector) The release of small area estimates should follow the standard ABS guidelines.. While the fine level of geography increases the risk of identification, this risk may to some extent be mitigated by the inherent smoothing of the data and additional model error introduced by the modeling process itself. However this does not mean that all caution can be thrown to the wind. Most small area projects will be commissioned by external agencies and individuals in these or other agencies may be realistically expected to be in a position to obtain knowledge of the models used to produce the small area estimates. Such information could possibly be used to identify individuals. Another issue is that although most small area estimates will be modeled and hence incur model error and further smoothing, there is the risk that an individual is correctly identified from the data although using incorrect logic. There will still be a public perception that the Act has been breached. In conclusion, all possible steps to avoid disclosure should be taken in preparing small area data for release and the Data Access and Confidentiality Methodology Unit should be consulted prior to release. Australian Bureau of Statistics 24
A Guide to Small Area Estimation - Version 1.1 05/05/2006 4. Choice of Small Area Techniques 4.1 Types of Small Area Estimation Techniques In this section we discuss some of the more common techniques available for small area estimation. We consider these techniques under the general headings of "Simple Small area Methods" (Section 4.1.1) and "Regression Methods" (Section 4.1.2). Although the methods discussed under Section 4.1.1 can be formulated in terms of a regression model, and hence would conceptually belong under Section 4.1.2, we have treated them separately because they are: 1. 2. simple to implement and require less statistical expertise. They are also commonly used to produce small area estimates by many government agencies. they are often appropriate as an initial exercise to obtain "rough" small area estimates, before attempting more rigorous techniques 4.1.1 Simple Small Area Methods Here we discuss the simpler methods that involve weighted survey estimates derived for a given level of geography that can be applied without the explicit application of statistical models. These methods include the: ! Direct Estimator Direct estimates are classical design-based estimators that are obtained by applying survey weights to the sample units in each small area (Saei and Chambers, 2003). Since most ABS surveys are designed to provide reliable estimates only at the national or state levels, sample sizes are often too small at the small area level to produce reliable direct estimates. Small area estimation is therefore concerned with alternative techniques that can produce small area estimates with higher accuracy than that of direct estimates. ! Broad Area Ratio Estimator (BARE) This estimator is one of the simplest types of synthetic estimators. It is calculated by pro-rating a broad area direct estimate by the ratio of the small area to broad area populations. This estimator applies the reliable broad area estimate proportionately across all small areas contained in the broad region. The success of the BARE estimator hinges largely on the choice of the broad area. The broad area needs to be chosen large enough to afford a direct estimate that is sufficiently reliable but small enough that all small areas within the broad area are sufficiently homogenous in the characteristic of interest. It is important to note that if small areas are in fact not homogenous within the broad region then the BARE will be biased. In practice this is difficult to verify hence caution should be exercised when using the BARE. It should only be used when users are aware of, and fully prepared to accept this assumption. Australian Bureau of Statistics 25
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