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

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A Guide to Small Area Estimation - Version 1.1 05/05/2006 o o o This guide aims to give advice for National Statistical Centres and regional offices on how to advise, respond to and incorporate small area estimates into their work, so they can apply simple models themselves and know when to draw on methodological skills for more complex models. A second volume of the manual will cover the more technical aspects of small area estimation and will be primarily aimed at methodologists and technical analysts involved in producing modeled small area estimates. The technical manual will cover in more detail the methodological and statistical issues that arise in small area estimation. The content of this manual contains material on the application of basic statistical models. The manual therefore assumes the reader has a basic familiarity with the theory and application of such models. Some parts of the manual contain references to somewhat more advanced methods. In such instances warning boxes strongly recommend to the reader that further methodological advice should be obtained from Methodology Division (ABS) before applying such techniques. What it is - A Guide to: o o o o o o what issues need to be thought through before undertaking a small area exercise, the methods and techniques available in small area estimation, the relative advantages and disadvantages and assumptions involved in each, who to talk to, who has implemented specific approaches into practice already and where to find relevant documentation, the trips and traps of putting various techniques into practice, how to best measure the reliability of small area predictions, how to detect model miss-specification and what diagnostics are available for assessing the overall quality of small area estimates. What it is not o An up-to- date encyclopedia of all the literature on small area techniques. The focus of this manual is much more on the practice of small area estimation in the production of government statistics. Compiling and maintaining an up-to-date summary of the technical literature would be highly resource intensive as the field is relatively new and rapidly evolving. It would also make it more difficult for the practitioner to access. Finally, we emphasise that this manual has been written under the assumption that the primary goal of small area data users is to obtain descriptive statistics of the relative characteristics of small areas rather than obtain the form of some dynamic structural process which generates those small area characteristics. The manual is therefore premised upon a descriptive framework for the ultimate decision making objectives, even though analytical methods are used to construct the models used to predict those small area characteristics. In other words, we assume users are primarily interested in the predictions from those models, not just the form and structure of the models per se. Australian Bureau of Statistics 8
A Guide to Small Area Estimation - Version 1.1 05/05/2006 1.4 What are the primary uses for Small Area Estimates? Federal, state and local government bodies involved in program funding / evaluation or regional planning are typically the primary users of ABS small area data. They require estimates of specified accuracy to assist them in making informed decisions on how to allocate resources or apply for additional resources. The need for government services to justify their decision making and be accountable to the community is seen as a very important factor. Small area estimates are often used by program administrators to determine or benchmark their funding allocations. Without the small area information, the administrators have difficulty in assessing the actual need for goods and services in each area. This can result in undesirable scenarios such as "the squeaky wheel gets the grease", whereby interest groups or areas which are most vocal receive a greater share of the funding allocations. Small area estimates provide detailed information on each area allowing for objective and informed decision making. Local government demand for small area data has also increased as they become increasingly aware and interested in the role statistics can play in informing them about what is happening in their own jurisdictions. 1.5 When should Small Area Estimates be Produced? Small area estimates should only be produced when there is strong and justified user demand as well as no alternate data at the small area level that will serve the required purpose. In addition there needs to be adequate survey and auxiliary data to ensure that the outputs produced will be of sufficient quality to fit their intended purpose. Small area estimates should primarily be considered where key policy making decisions require discerning between relative needs of different small areas and such information does not currently exist or requires updating (eg. Disability data). To develop small area estimates, significant resources in staff time to develop, check and get approval for release is needed. The complexity of most small area estimation exercises and the difficulty in validating the reliability of the output makes it very difficult to fully automate the production process. To a large extent, each small area undertaking has to be tailored to the nature and specifics of the problem at hand. Therefore, care needs to be taken to ensure the need for the small area estimates warrants the effort required. The first step is to discuss with the users to see if state or part of state estimates would be adequate. If there is not much variation between the small areas then more broad estimates would be adequate. It is also worth investigating any sources of administrative data that can be used as auxiliary data for a small area model. Finally, it is worthwhile checking that the chosen small area model fitted to the data is appropriate for that data and inherent assumptions in the model do at least approximately hold. For example fitting a linear model to the data would require that the errors are identically and independently distributed with zero mean and constant variance. It is therefore prudent to check such assumptions are reasonable and have been satisfied before estimating the model. Australian Bureau of Statistics 9
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