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Measuring Uncertainty in Population Estimates at Local Authority Level

This project aims to improve the understanding, measurement, and reporting of the quality of population estimates at the local authority (LA) level. We utilize a mixed-methods approach combining census data, administrative sources, and surveys to obtain annual population estimates. Key components influencing uncertainty include internal and international migration, for which we provide a comprehensive error analysis. The resulting quality measures will allow LAs to assess and enhance their estimates, fostering better data-driven decision-making.

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Measuring Uncertainty in Population Estimates at Local Authority Level

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  1. Measuring Uncertainty inPopulation Estimatesat Local Authority Level Ruth Fulton, Bex Newell, Dorothee Schneider

  2. Outline • Project aim • Overall method • Method internal migration • Method international migration • Outputs

  3. Project aim Improve understanding, measurement and reporting of the quality of population estimates at LA level • Obtain overall quality measures for annual population estimates at LA level

  4. Mid-Year Population Estimates • Cohort component method • Pop.(t) = pop.(t-1)+ births • – deaths + internal net migration + international net migration • Determining associated uncertainty is complex • Mixed sources: Census, administrative sources, surveys • Different estimation methods

  5. Error (t) = error (t-1) • + error (net internal migration) • + error (net international migration) Measuring uncertainty: Overall method • Components with biggest impact: • 2001 Census-based estimate • Internal migration • International migration • Estimate distribution of error for component • Combine error estimates into overall quality measure for MYE at LA level

  6. Internal migration • Estimates based on GP registration data • Sources of uncertainty in estimates related to: • Migrants missing from GP register • Time lags between moving and re-registration • Double counting of school boarders

  7. Method for internal migration • Benchmark approach • Uses adjusted 2001 Census data as benchmark • Applies model from 2001 to subsequent years • Limitation – does not cover all quality issues

  8. Method for internal migration (ctd.) • Movers in Census: those with other address one year ago • Movers in PRDS: those with different addresses in two downloads • Census data adjusted to be as similar to PRDS data as possible • Compare observed number of migrants to a ‘true’ number of migrants • Error represented by scaling factor of truth (Census)/PRDS

  9. Age pattern • log(Census/PRDS) • shows double counting of school boarders • shows undercount of young male migrants

  10. Mean log(Scaling Factors)Inflows Geographical variation • Scaling factors vary by area • Undercount in urban areas or areas with high proportion of students • Cluster analysis

  11. Model • Fit model to log of scaling factors of groups of LAs • Obtain predicted values and residuals  Error measure is obtained by simulating from this distribution

  12. Distribution of estimated inflows

  13. International migration • Focuses on intentions-based IPS estimates • Multi-stage approach to distribute to national estimates to lower levels of geography

  14. International migration (ctd) • Produce error distribution for statistical error • Bootstrapping approach • Resampling IPS • Resampling LFS (regional level) • Reproduce estimation method with new samples

  15. Outputs Outputs • Composite quality measure will be derived from the overall error distribution • LAs will be banded based on this measure

  16. Contact dorothee.schneider@ons.gsi.gov.uk imps@ons.gsi.gov.uk Questions?

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