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Subnational population forecasts: Do users want to know about uncertainty?

Author

Listed:
  • Tom Wilson

    (Independent researcher)

  • Fiona Shalley

    (Charles Darwin University)

Abstract

Background: Subnational population forecasts form a key input to many significant investment and planning decisions, but these forecasts are often subject to considerable amounts of uncertainty, even in the short run. This uncertainty is rarely quantified at the subnational scale, and little attention has been given to how uncertainty can be effectively communicated to users. Objective: We wished to find out if users of subnational population forecasts want to know about forecast uncertainty, their understanding of uncertainty, and their views on various methods of communicating it. Methods: An online survey of users of population forecasts in Australia was undertaken, followed by focus groups to permit in-depth discussions of forecasting and uncertainty topics. Results: Nine out of ten survey respondents wanted uncertainty information. The majority also understood basic uncertainty concepts, although about one-third did not currently use any uncertainty information. Several demographic terms for populations and uncertainty were found to be confusing to users. Conclusions: Discovering that uncertainty information is desired by most users is an encouraging finding. Further work is required to create fully developed models for doing so. Learning how users interpret various means of expressing uncertainty helps in designing more effective communication tools. Contribution: The paper makes an original contribution to the forecast uncertainty literature through its focus on forecast users. We present the results of a recent survey and focus groups of subnational population forecast users in Australia which asked for their views on forecast uncertainty.

Suggested Citation

  • Tom Wilson & Fiona Shalley, 2019. "Subnational population forecasts: Do users want to know about uncertainty?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(13), pages 367-392.
  • Handle: RePEc:dem:demres:v:41:y:2019:i:13
    DOI: 10.4054/DemRes.2019.41.13
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    References listed on IDEAS

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    5. Tom Wilson & Huw Brokensha & Francisco Rowe & Ludi Simpson, 2018. "Insights from the Evaluation of Past Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 137-155, February.
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    Cited by:

    1. Wilson, Tom & Grossman, Irina & Temple, Jeromey, 2023. "Evaluation of the best M4 competition methods for small area population forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 110-122.
    2. Sigurd Dyrting & Andrew Taylor & Tom Wilson, 2024. "Application of P-TOPALS for Smoothing Input Data for Population Projections ‘At the Edge’," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 43(2), pages 1-28, April.
    3. Philip Rees & Tom Wilson, 2023. "Accuracy of Local Authority Population Forecasts Produced by a New Minimal Data Model: A Case Study of England," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(6), pages 1-30, December.
    4. Grossman, Irina & Wilson, Tom & Temple, Jeromey, 2023. "Forecasting small area populations with long short-term memory networks," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    5. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.

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    More about this item

    Keywords

    population forecasting; forecast errors; uncertainty; forecast interval; Australia; science; communication;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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