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Stochastic population forecasts for the United Kingdom

This article discusses the development of a stochastic population forecasting model for the United Kingdom, which allows for the specification of uncertainty in population projections. It highlights the use of probability distributions to estimate future population dynamics and presents provisional results for age structures and dependency ratios. The limitations of the model are acknowledged, but stochastic forecasting is deemed a useful approach with awareness of its limitations.

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Stochastic population forecasts for the United Kingdom

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  1. Stochastic population forecasts for the United Kingdom Emma Wright & Mita Saha Office for National Statistics

  2. National population projections • Dependent on assumptions about future levels of fertility, mortality and migration which are reviewed every two years • Latest projections based on the population at mid-2006 • Results on GAD website and National Statistics Online

  3. Uncertainty in population projections • Demographic behaviour is inherently uncertain • Any set of projections will inevitably be proved wrong to a greater or lesser extent

  4. Past UK population projections

  5. Mean projection error by age groupPast UK projections

  6. Principal & variant projections • Principal projections - based on assumptions thought to be the best at the time they are adopted • Variant projections – plausible alternative scenarios, NOT upper or lower limits. • Limitation - principal and variant projections are deterministic, no measure of probability

  7. Total UK Population2006-based principal and variant projections

  8. ONS Stochastic forecasting project • Aim • To develop a model that will enable the degree of • uncertainty in UK national population projections to • be specified • Approach • Express fertility, mortality and migration assumptions in terms of probability distributions • Generate random values from these probability distributions to produce predictive distributions for any projection result

  9. Probability distributions How can we estimate future probability distributions? Three approaches: • Analysis of accuracy of past projections • Expert opinion • Time series analysis No ‘right’ answer – subjective judgement

  10. Model Drivers • Fertility – Total Fertility Rate • Mortality – Male and female period life expectancy at birth • Migration – Total net migration

  11. Deriving probability distributionsfor the ONS model • Expert opinion - NPP expert advisory group questionnaire • Past projection errors - GAD historic projections database

  12. Expert Opinion • National Population Projections Expert Advisory Group (set up via BSPS): • David Coleman Phil Rees • Mike Murphy Robert Wright • John Salt John Hollis • Expressed opinions on the most likely levels and 67% confidence intervals for TFR, period life expectancy at birth and net migration in 2010 and 2030.

  13. Generating sample paths Random walk with drift model: Driver(T)= Driver(T-1) + ValueDriver(T) + DriftDriver(T)

  14. UK TFR250 sample paths with 67% confidence intervals

  15. UK TFRProbability distribution v 2006-based assumptions

  16. UK male period life expectancy at birth Probability distribution

  17. UK net migration Probability distribution

  18. Program • Based on cohort component model • UK only • Random numbers generated • Age distributions • 5,000 simulations • 2006-2056 projection period

  19. Provisional results UK age structure 2031

  20. Provisional resultsUK age structure 2056

  21. Provisional results: UK total dependency ratioPredictive intervals

  22. Provisional results: Probability of the number of children in the UK exceeding the SPA population

  23. Illustrative probabilities • Based on current provisional assumptions, there is a…. • 48% chance that TFR will exceed replacement level • 9% chance that male period life expectancy at birth will exceed 90 yrs • 20% chance that there will be negative annual net migration • 2% chance that the population will fall below the 2006 base level • … at some point between 2006 and 2056.

  24. Limitations • Do not know true probability distributions • Validity of results wholly dependent on assumptions underlying model • Inflated sense of precision • Communicating results and limitations may be a challenge • BUT….if aware of the limitations, then stochastic forecasting can be a useful approach

  25. Estimates of the UK TFR in 2049/2050Median and 80% confidence intervals

  26. Quality Assurance • Prof Phil Rees (University of Leeds) • Prof Nico Keilman (University of Oslo) • Prof Wolfgang Lutz (Vienna Institute of Demography) • ONS Methodology Directorate

  27. Future plans • ONS plans to publish a set of 2006-based stochastic forecasts for the UK as ‘Experimental Statistics’ during 2009 • If you would like to feed in any comments on this work, please e-mail: natpopproj@ons.gov.uk

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