Transcript Measuring uncertainty in the Local Authority Population Estimates

Measuring Uncertainty in
Population Estimates
at Local Authority Level
Ruth Fulton, Bex Newell, Dorothee Schneider
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Outline
• Project aim
• Overall method
• Method internal migration
• Method international migration
• Outputs
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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
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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
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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
Error (t) = error (t-1)
+ error (net internal migration)
+ error (net international migration)
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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
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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
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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
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Age pattern
Mean log scaling factors for inflows by age and sex
0.4
0.3
Mean lsf
0.2
0.1
0.0
-0.1
-0.2
Males
-0.3
Females
-0.4
0
10
20
30
40
50
60
70
80
Age
• log(Census/PRDS)
• shows double counting of school boarders
• shows undercount of young male migrants
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Geographical variation
- Scaling factors vary by
area
Mean log(Scaling Factors)
Inflows
- Undercount in urban
areas or areas with high
proportion of students
- Cluster analysis
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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
.5
0
-.5
Mean predicted log(sf)
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Mean predicted log scaling factors, Inflows male
0
20
40
60
age
group 1
group 3
group 2
group 4
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Distribution of estimated inflows
example LA
0
5.0e-04
.001
Density
.0015
.002
green: PRDS flow, red: Census (observed truth)
6000
6500
7000
estimated 'true' flow
7500
8000
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International migration
• Focuses on intentions-based IPS estimates
• Multi-stage approach to distribute to national estimates
to lower levels of geography
National
IPS direct estimate
Regional
Calibrate to Labour Force Survey (LFS)
3 year average
Intermediate (NMGi)
Distribute using IPS 3 year average
Local Authority (LA)
Distribute using immigration model
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International migration (ctd)
• Produce error distribution for statistical error
Bootstrapping approach
Resampling IPS
Resampling LFS (regional level)
Reproduce estimation method with new samples
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Outputs
Outputs
• Composite quality measure will be derived from the
overall error distribution
• LAs will be banded based on this measure
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Questions?
Contact
[email protected]
[email protected]
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