1 Measuring Quality Issues Associated with Internal Migration Estimates Joanne Clements, Amir Islam, Ruth Fulton & Jane Naylor Demographics Methods Centre and Centre for Demography

2 Outline Background Internal Migration Quality Issues Research methods Findings Issues arising Next Steps

3 Project Improve understanding, measurement and reporting of the quality of population estimates

4 Context Debate about amount of uncertainty in population estimates Improving Migration and Population Statistics (IMPS) Project – Quality strand ‘ONS should flag the level of reliability of individual local authority population estimates’ (UK Statistics Authority) Leading new international research

5 Key Methodology Points Map out the procedures and data sources used to derive population estimates Identify associated quality issues Attempt to quantify uncertainty using statistical theory & empirical evidence instead of expert opinion Combine individual measures of uncertainty by simulating potential errors in the data

6 Key Methodology Points Map out the procedures and data sources used to derive population estimates Identify associated quality issues Attempt to quantify uncertainty using statistical theory & empirical evidence instead of expert opinion Combine individual measures of uncertainty by simulating potential errors in the data

7 Key Methodology Points Map out the procedures and data sources used to derive population estimates Identify associated quality issues Attempt to quantify uncertainty using statistical theory & empirical evidence instead of expert opinion Combine individual measures of uncertainty by simulating potential errors in the data

8 Key Methodology Points Map out the procedures and data sources used to derive population estimates Identify associated quality issues Attempt to quantify uncertainty using statistical theory & empirical evidence instead of expert opinion Combine individual measures of uncertainty by simulating potential errors in the data

9 Progress Initial work proved feasibility of simulation methodology Focus now on sources of error with greatest impact; internal and international migration Currently focussing on internal migration

10 Internal Migration Methodology Individual moves captured from GP re- registration data Annual (end July) download of patient registers Moves identified from changes with previous year’s download. Local authority moves constrained to information provided by NHS Central Register

11 Key Internal Migration Quality Issues Source LA for out-flows to NI and Scotland Census and 2001 Patient Registers Constraining GP register data to NHSCR data Time Lags Double counting of School boarders Not registered at mid-year

12 Key Internal Migration Quality Issues Time lags between moving and reregistering Moves not captured by GP registers because patients were not registered at one annual download Constraining GP register data to NHSCR Variation in 2001 base population in the Census and patient registers Potential double counting of school boarders Out-flows to Scotland / Northern Ireland and allocating these to LAs

13 Research Methods A review of relevant literature. Local authority level data analysis Review any internal quality assurance. Sensitivity analysis

14 Re-registration Time Lag Research Comparison of mid-2001 internal migration estimates with 2001 Census migration estimates Sex ratios Propensity to migrate Comparison with other data sources Investigating ‘bumps’ in population age profiles that sustain over time

15 Birmingham Population Age Profile

16 Provisional Time Lag Findings: Sex Ratios Evidence of late-registration of young male migrants Geographic variation in sex ratio differences and therefore time lags SourceMigrant Sex Ratio years Census Internal Migration 00/

17 Provisional Time Lag Findings: Propensity to Migrate GP List inflation invalidates analysis to compare Census and internal migration propensities Instead, comparing migrant counts for similar populations to identify possible time lags Census doesn’t always produce higher LA internal migration estimates

18 Provisional Time Lag Findings: Other Data Sources Limited other data sources with which to compare with – No major differences with comparator data sources Evidence from survey data of significant late registration (Median 4 months)

19 Provisional Time Lag Findings: Age Profiles Some LAs do have age profile bumps that sustain (particularly young adults ages) Patterns vary again geographically Possibly due to: Imbalance between in and out migrants in LAs with higher education institutions (Males especially) Increases in International immigrants (young males again)

20 Provisional Time Lag Findings: Summary Evidence of Age-Sex Specific Time lags in re- registration. Evidence that these vary geographically. Unclear how much year on year time lags cancel each other out. Next Step is to produce an potential error distribution

21 School Boarder Research LAs with largest school boarder populations chosen to identify possible double counting Comparing age profile changes in school boarders with LA internal migration estimates

22 Provisional School Boarder Findings Similar patterns between school boarder arrivals and internal in-migration Therefore, strong evidence of double counting Difficult to estimate accurately due to data issues Limited impact, for most LAs, on all age internal migration estimates

23 Challenge: Deriving Error Distributions For Each Quality Issue Lack of suitable data Conflicting evidence Somewhat subjective choice of error bounds -Bias towards larger errors? -Sensitivity Testing -Constraining -Correlation -User Feedback

24 Challenge: Interpretation of Findings In reality, there is uncertainty in these measures of uncertainty, as… –Only as good as the error assumptions made for each issue Therefore exact findings are misleading Present approximate indicators

25 Reporting and Future Work Short update on progress – August 2009 Detailed papers on internal migration findings - November Potential further work: - international migration - quantifying impact of methodological changes on quality of estimates