1 understanding and measuring uncertainty associated with the mid-year population estimates joanne...

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Understanding and Measuring Uncertainty Associated with the Mid-Year Population Estimates

Joanne Clements

Ruth Fulton

Alison Whitworth

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Context

• Improving Migration and Population Statistics (IMPS) Project

• Quality Strand

• “Establish quality measures for population statistics”

• No international precedent for this work

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Issues

• Estimates compiled from a wide range of administrative sources plus some survey and Census data

• Source data subject to sampling and non-sampling errors

• Lack of independent data with which to corroborate

• How to estimate each potential error and combine these in one measure?

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Aim and Objectives

• AimImprove understanding, measurement and reporting

of the quality of population estimates

• Objectives– Describing the sources of uncertainty– Developing methods for measuring uncertainty for

each issue and combining them into one measure– Eventually feeding findings into ONS quality

reports

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Presentation Outline

• Summarise population estimates methodology

• Summarise previous research on quality• Detail proposed error measurement

methodology– Illustrate by applying to local authority (LA) mid-

2006 population estimates

• Outline emerging proposals for further work to achieve robust quality measures

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Calculating LA Population Estimates

e.g. Southampton UA

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Calculating LA Population Estimates (cont)

Adjustment

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Calculating LA Population Estimates

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Previous Research

Quality of Population Estimates• Past experience of inter-censal errors • Sampling error and expert opinion of non-

sampling error in components of estimates

Quality of Population Projections• Accuracy of past projections• Use of variant projections• Simulation methods using error distributions

for the components of projections (stochastic forecasting)

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Proposed Methodology: Initial Assessment of Quality Issues

• Map out the procedures and data sources used to derive population estimates

• Identify associated quality issues

• Identify the importance of these issues

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LA Population Estimates:Initial Assessment of Quality Issues

• Brief assessment of the evidence for each component

• For example: Internal Migration– Relies on GP registration data– Assumes patients reregister within a month of

moving (known issue for students leaving university)

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Proposed Methodology (cont):Detailed Investigation of Quality Issues

• Quantify uncertainty using statistical theory, empirical evidence and / or expert opinion

• Both sampling and non-sampling errors

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LA Population Estimates:Detailed Investigation of Quality Issues

• Attributing a potential uncertainty range and distribution to each component

• Not each quality issue

• Made relatively simplistic distribution assumptions (Normal or Uniform)

• Assumed same level of uncertainty across LAs

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LA Population Estimates:Detailed Investigation of Quality Issues

• Estimating uncertainty relative to size of local authority component

• For example: Could assume potential error in annual local authority births estimate

N(0, X% of estimated births)

– Assume similar error distributions by year

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Proposed Methodology (cont):Overall Quality Measure

• Mathematically complicated to combine a large number of potential error measures into one quality measure– Errors may be correlated– Distributions not all normally distributed

• Developed a Simulation methodology

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LA Population Estimates: Simulation

• For each local authority randomly generate errors for each component – Using previously developed error distributions

Mid-2001 error estimate+ Births 01/02 error estimate - Deaths 01/02 error estimate+ Internal In-Migrants 01/02 error estimate - Internal Out-Migrants 01/02 error estimate+ …..+ Births 02/03 error estimate - Deaths 02/03 error estimate+ …

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LA Population Estimates: Simulation

• Calculate error in mid-2006 estimate by combining the errors generated for each component in each year up to 2006

• Repeat process 1000 times

• Obtain distribution of potential error in mid-2006 local authority estimate

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Findings: Potential Error Distribution

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Findings: Measuring Uncertainty in Population Estimates

• Simulation methodology allows measures of uncertainty to be calculated for population estimates.

• But, in reality, there is uncertainty in these measures of uncertainty, as…– Only as good as the error assumptions made for

each issue / component of change– Very difficult to exactly measure non-sampling

error

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Findings: Key Components of Uncertainty

• Uncertainty in population estimates related to:– Size of each component– Error distribution assumptions

• Key components driving uncertainty in LA estimates:– Mid-2001 base population estimate– Internal Migration– International Migration (IPS)– Specific components important in specific LAs

e.g. Foreign Armed Forces

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Extending the Methodology

• Current assumptions in the estimation of uncertainties are inadequate– Need to examine issues within each component– Consider LA variation in uncertainties within each

component

• Currently focussing on refining error distributions for key drivers of uncertainty within LA estimates– Internal Migration– International Migration

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Estimating Uncertainty - Internal Migration:Emerging Proposals for Further Work

Building upon previous work, investigate

uncertainty in estimates related to:• Time lags between moving and reregistering• Moves not captured by GP registers because

patients were not registered when data were extracted

• The scale of constraining GP register data to NHSCR

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Estimating Uncertainty - International Migration:Emerging Proposals for Further Work

• Calculating sampling error of IPS estimates• Investigating uncertainty around migrant and

visitor switcher estimates • Investigate uncertainty within methods used

to calculate LA migration estimates from the IPS– For example, in LA emigration model used

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Future Outcomes of this Work

• Increased understanding of sources of error in the population estimates and their relative importance

• Ability to focus resources for research on key sources of uncertainties

• Additional information which could feed into Quality Reports

This work is intended to improve our understanding of the uncertainty in population estimates, rather than provide exact estimates of uncertainty

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SummaryMeasuring Uncertainty of Population Estimates

• Estimating their error margin is complex• Detailed quality assessment of each

component required to obtain a robust measurement

• Simulation methods are a plausible approach to approximately measure the overall quality of an estimate

• Ongoing work on estimating uncertainty in migration components