Chapter 24 Quality of the National Accounts
24.1 'Quality' in relation to statistics is a multidimensional concept which embodies the notion of 'fitness for purpose'. In order to assist and encourage informed decision making, statistics need to be not only as accurate as possible, but also timely and relevant. There are often trade-offs between the various aspects of quality, and in order to make economic statistics timely enough to be relevant indicators for the analysis of current or recent economic conditions this is likely to be at the expense of some degree of accuracy. The ABS, in consultation with data users, aims to optimise the various aspects of quality.
24.2 The national accounts program is discussed against the seven quality dimensions of the ABS Data Quality Framework. These dimensions are a view of data quality aspects that determine fitness for purpose and relate to the institutional environment, relevance, accuracy and reliability, timeliness, accessibility, interpretability, and coherence. As well as informing users about quality, the framework also provides feedback to ongoing quality improvement programs within the ABS.
24.3 Underlying these dimensions of quality is the notion of integrity – that statistical policies and practices are guided by ethical standards and professional principles which are transparent. The integrity of the ABS is underpinned by legislation within which the organisation operates, and its willingness to subject its operations and performance to both internal and external scrutiny. The principal legislation determining the functions and responsibilities of the ABS are the Australian Bureau of Statistics Act 1975 and the Census and Statistics Act 1905. These Acts provide that the ABS is headed by the Australian Statistician – a statutory office with an independent status and the authority to conduct statistical collections.
24.4 This chapter describes each of the aspects of quality and assesses the national accounts against them. Compilation of the national accounts is a complex task involving many diverse data sources. It is not possible to provide a single, comprehensive measure of the quality of the estimates. Nonetheless, it is possible to gain an insight into their quality by analysing each of the aspects of quality. To obtain an overall picture, all aspects need to be considered together. However, different users may weight each of the aspects differently, and within each aspect what satisfies one user may not satisfy another. Thus, two users may look at the same set of statistics, with one considering them to be of good quality while the other may think that there are quality deficiencies.