IDEAS home Printed from https://ideas.repec.org/a/kap/poprpr/v42y2023i6d10.1007_s11113-023-09839-2.html
   My bibliography  Save this article

Accuracy of Local Authority Population Forecasts Produced by a New Minimal Data Model: A Case Study of England

Author

Listed:
  • Philip Rees

    (University of Leeds)

  • Tom Wilson

    (The University of Melbourne)

Abstract

The preparation of forecasts for small and local area populations involves many challenges. Standard cohort-component models are problematic because of small numbers, which make estimation of rates unreliable. Because of this, the Synthetic Migration Population Projection (SYMPOPP) model was designed to forecast local populations without need for detailed area-specific information. This model had been used successfully for small area forecasts in Australia. The objective of the paper is to assess its performance when applied to local areas in England. The model uses a bi-regional structure based on a movement population account. Sub-models of total population change are employed to control future change. Fertility, mortality and migration rates are borrowed from national statistics, constrained to small area indicators. The model uses an Excel workbook with VBA routines and is relatively easy and quick to use. Model inputs were calibrated for 2006–2011 and used to forecast for 2011–2021. Results were tested against the census-based 2021 mid-year populations. A new error statistic, Age Structure Error, was used to evaluate Basic and Refined model versions against official projections. The two versions of SYMPOPP posted lower errors. The simple models had fewer areas with errors of 10% or more (12.3–12.6%) compared with the official projections (14.5% of areas). Investigation revealed that these errors occurred in local authorities with high military, student, prison, or ethnic minority populations, influenced by factors not captured in a projection model for the general population.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:poprpr:v:42:y:2023:i:6:d:10.1007_s11113-023-09839-2
    DOI: 10.1007/s11113-023-09839-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11113-023-09839-2
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11113-023-09839-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. repec:cai:popine:popu_p1973_28n1_0129 is not listed on IDEAS
    3. Erengul Dodd & Jonathan J. Forster & Jakub Bijak & Peter W. F. Smith, 2021. "Stochastic modelling and projection of mortality improvements using a hybrid parametric/semi-parametric age–period–cohort model," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2021(2), pages 134-155, February.
    4. Mathew E. Hauer & Carl P. Schmertmann, 2020. "Population Pyramids Yield Accurate Estimates of Total Fertility Rates," Demography, Springer;Population Association of America (PAA), vol. 57(1), pages 221-241, February.
    5. Aude Bernard & Martin Bell, 2015. "Smoothing internal migration age profiles for comparative research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(33), pages 915-948.
    6. Jeff Tayman, 2011. "Assessing Uncertainty in Small Area Forecasts: State of the Practice and Implementation Strategy," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 30(5), pages 781-800, October.
    7. 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.
    8. Stanley Smith, 1986. "Accounting for migration in cohort-component projections of state and local populations," Demography, Springer;Population Association of America (PAA), vol. 23(1), pages 127-135, February.
    9. Stephan Kolassa & Wolfgang Schütz, 2007. "Advantages of the MAD/Mean Ratio over the MAPE," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 40-43, Spring.
    10. Joseph Harrison & Katherine Lisa Keenan & Frank Sullivan & Hill Kulu, 2023. "Union formation and fertility amongst immigrants from Pakistan and their descendants in the United Kingdom: A multichannel sequence analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(10), pages 271-320.
    11. 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.
    12. 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.
    13. Mario Reinhold & Stephan Thomsen, 2015. "Subnational Population Projections by Age: An Evaluation of Combined Forecast Techniques," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(4), pages 593-613, August.
    14. 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.
    15. Stanley Smith & Jeff Tayman, 2003. "An evaluation of population projections by age," Demography, Springer;Population Association of America (PAA), vol. 40(4), pages 741-757, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Michael P. Cameron & William Cochrane, 2015. "Using Land-Use Modelling to Statistically Downscale Population Projections to Small Areas," Working Papers in Economics 15/12, University of Waikato.
    6. 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.
    7. 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.
    8. Jeff Tayman & David A. Swanson, 2017. "Using modified cohort change and child-woman ratios in the Hamilton–Perry forecasting method," Journal of Population Research, Springer, vol. 34(3), pages 209-231, September.
    9. 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).
    10. Jack Baker & David Swanson & Jeff Tayman, 2023. "Boosted Regression Trees for Small-Area Population Forecasting," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-24, August.
    11. Jack Baker & David Swanson & Jeff Tayman, 2021. "The Accuracy of Hamilton–Perry Population Projections for Census Tracts in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(6), pages 1341-1354, December.
    12. 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.
    13. Patrice Dion, 2017. "An Alternative to Fixed Transition Probabilities for the Projection of Interprovincial Migration in Canada," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(6), pages 871-901, December.
    14. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    15. Huan Wang & Jianyuan Huang & Shuangyue Sun, 2019. "Assessment of the Financial Sustainability of China’s New Rural Pension Plan: Does the Demographic Policy Reform Matter?," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    16. Aude Bernard & Martin Bell, 2018. "Internal migration and education: A cross-national comparison," Papers 1812.08913, arXiv.org.
    17. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    18. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    19. Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
    20. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:poprpr:v:42:y:2023:i:6:d:10.1007_s11113-023-09839-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.