Application of P-TOPALS for smoothing input data for population projections ‘at the Edge’

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Abstract

Sparsely populated areas of developed countries are regions of great demographic diversity and dynamism. While they remain strategically and economically important, trends in urbanization and technology have increased their relative sparsity and isolation making centralized government, service delivery and planning a challenge. Populations of their sub-jurisdictions are small and often exhibit significant heterogeneity in key demographic characteristics, not least between their Indigenous first residents and non-Indigenous citizens. Development of projection models for these areas is challenged by significant input data paucity, biases and structural issues related to the data collection and estimation architectures in place to gather input data across diverse and small populations. While this is the case, the demand for and importance of projections is no less for sparsely populated areas than elsewhere. Variants of the cohort component model are important tools for population projections for SPAs, with their grounding in the demographic accounting equation and modest input requirements. Nevertheless, to attain fit-for-purpose input data requires demographers to consider and select from a growing number of methods for smoothing issues with input data for projections for these regions. In this article we analyze the contributions of recent advances in methods for estimating fertility, mortality, and migration rates for small and diverse populations such as those in SPAs, focusing on the very sparsely populated jurisdiction of the Northern Territory of Australia. In addition to the contributions of our method itself, results at the detailed level demonstrate how abnormal and challenging ‘doing’ projections for sparsely populated areas can be.
Original languageEnglish
Article number31
Pages (from-to)1-28
Number of pages28
JournalPopulation Research and Policy Review
Volume43
DOIs
Publication statusPublished - Mar 2024

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