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HomeResearchPublicationsCreating a Sense of 'closure': Providing Confidence Intervals On Some Recent Estimates of Indigenous Populations
Creating a sense of 'closure': Providing confidence intervals on some recent estimates of Indigenous populations
Author/editor: Hunter, B, Dungey, MH
Year published: 2003
Issue no.: 244

Abstract

The 'error of closure' is the population growth that cannot be accounted for either by natural increase or by quantifiable non-demographic factors. The term is somewhat misleading: since it incorporates all unquantifiable components of the increase in a population count, it is unlikely ever to be 'closed'.

This study highlights the significance of variability of Indigenous population estimates by calculating standard errors, one of the conventional measures of reliability of statistics. That is, with tongue firmly in cheek, a sense of 'closure' is created in the debate by documenting the variability of estimates.

We introduce the Dual System Estimator method for estimating the Indigenous population, and review the international literature on its strengths and weaknesses. Once Australia's Indigenous population has been estimated using this method, confidence intervals are compared to those produced using the traditional undercount method. The main conclusion is that Dual System Estimates of the Indigenous population are reasonably accurate at the national level. Unfortunately, this conclusion may need to be revised when regionally disaggregated data are examined.

The central theme of this paper is that policy makers need to take into account the fact that Indigenous population statistics from the census are merely estimates. Given the importance of this data in the horizontal fiscal equalisation funding formula, it is particularly important to have accurate estimates for each of the States and Territories. Obviously, Dual System Estimates are not a panacea, but they are an alternative method of benchmarking the Indigenous population, and hence provide an appreciation of the reliability and bias of existing estimates.

ISBN: 0 7315 5619 4

ISSN:1036 1774

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