Aboriginal and Torres Strait Islander Population Projection Evaluation

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Journal Article

Wilson, T., Temple, J., Burchill, L. et al. (2024): Evaluation of alternative methods for forecasting the Aboriginal and Torres Strait Islander population of Australia. Genus 80, 16.

Abstract: Assessing future demand for a wide range of services requires good quality population forecasts. Unfortunately, many past forecasts of the Aboriginal and Torres Strait Islander (Indigenous) population of Australia have proved highly inaccurate. This is due to poor data quality, missing data, and demographers’ incomplete understanding of Indigenous population change. In addition, because Indigenous population estimates are published only every 5 years and long after the reference date, forecasts are often used as preliminary population estimates. These form the denominators of various metrics used to monitor programmes aimed at improving health and social outcomes. The aim of this paper is to present an evaluation of alternative forecasting models and forecasts of the Indigenous population of Australia’s States and Territories by age and sex. Four models, differing substantially in complexity, were evaluated: (1) the simple Hamilton–Perry method, (2) the synthetic migration cohort-component model, (3) a uniregional cohort-component model with net migration, and (4) a bi-regional cohort-component model. The population forecasting methods were evaluated against several criteria, including forecast accuracy over the 2016–21 period, input data requirements, conceptual adequacy, output detail, time required to prepare, ability to create scenarios and select alternative assumptions, and ease of implementation. The Hamilton–Perry and synthetic migration cohort-component models provided greater forecast accuracy and scored well against the evaluation criteria. In the challenging data environment for modelling Indigenous populations, simpler forecasting methods offer several practical advantages and are likely to produce more accurate forecasts than more data-intensive models.