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Preparing local area population forecasts using a bi-regional cohort-component model without the need for local migration data

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  • Tom Wilson

    (Independent researcher)

Abstract

Background: Cohort-component models incorporating directional migration are conceptually robust demographic models which are widely employed to forecast the populations of large subnational regions. However, they are difficult to apply at the local area scale. Simpler models, such as the Hamilton–Perry model, have modest input data requirements and are much quicker, cheaper, and easier to implement, but they offer less output detail, suffer from some conceptual and practical limitations, and can be less accurate. Objective: The aim of this paper is to describe and evaluate the synthetic migration cohort-component model – an approach to implementing the bi-regional model for local area population forecasts without the need for any locally specific migration data. Methods: The new approach is evaluated by creating several sets of ‘forecasts’ for local areas of Australia over past periods. For comparison, forecasts from two types of Hamilton–Perry model are also evaluated. Error is measured primarily with an alternative Absolute Percentage Error measure for total population which takes into account how well or poorly the population age–sex structure is forecast. Results: In the evaluation for Australian local areas, the synthetic migration model generated more accurate forecasts that the two Hamilton–Perry models in terms of median, mean, and 90th percentile Absolute Percentage Errors. Contribution: The synthetic migration model combines the conceptual and practical advantages of the bi-regional cohort-component model with the light data requirements and ease of calculation of simpler cohort models. It allows the bi-regional model to be applied in circumstances where local area migration data are unavailable or unreliable.

Suggested Citation

  • Tom Wilson, 2022. "Preparing local area population forecasts using a bi-regional cohort-component model without the need for local migration data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 46(32), pages 919-956.
    • Handle: RePEc:dem:demres:v:46:y:2022:i:32
    DOI: 10.4054/DemRes.2022.46.32
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    References listed on IDEAS

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    1. Jeff Tayman & David A. Swanson & Jack Baker, 2021. "Using Synthetic Adjustments and Controlling to Improve County Population Forecasts from the Hamilton–Perry Method," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1355-1383, December.
    2. Tom Wilson, 2016. "Evaluation of Alternative Cohort-Component Models for Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(2), pages 241-261, April.
    3. David Swanson & Alan Schlottmann & Bob Schmidt, 2010. "Forecasting the Population of Census Tracts by Age and Sex: An Example of the Hamilton–Perry Method in Action," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 29(1), pages 47-63, February.
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    Cited by:

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    More about this item

    Keywords

    cohort-component model; population forecasting; local area; synthetic migration;
    All these keywords.

    JEL classification:

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

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