1. Improving the estimation of long-term international emigration at local authority level Joshua Turner Population Statistics Research Unit (PSRU) Local Insight Reference Panels 1

2. Session Topics Brief Background Work so far Preliminary results: Impact of changes Current work Next steps 2

3. Brief Background 3

4. Importance of Emigration Birmingham Source: Population Estimates for UK, England and Wales, Scotland and Northern Ireland, Mid-2011 and Mid-2012, ONS Mid-2011 Population1,074,283 Births+17,636 Deaths-8,028 International In-Migration+11,710 International Out-Migration-7,002 Movers to elsewhere in UK-45,503 Movers from elsewhere in UK+42,338 Mid-2012 Population1,085,417 4

5. Brief Background No datasets with robust counts of emigration at Local Authority level Current method uses a model-based approach 5

6. Stepwise Model Stepwise model Relationship between IPS LA Level Emigration Estimates (3 Year Average) Predictor Variable 1 Variable 2 Variable i Variable 3 Variable 4 Variable 5 Predictor Variable 1 Variable 2 Variable 4 Variable 3 6

7. Predictor Variables Census 2011 Number of hostels (+) Number of people of North American country of birth (+) Number of people of Oceania country of birth (+) Number of people of African country of birth (+) Annual Population Survey (APS) Number of people aged 16+ in employment (-) Migrant Worker Scan (MWS) Number of in-migrants of EU8 nationality (+) 7

8. Poisson Regression Model Emigration estimates constrained to IPS totals Poisson Regression Model IPS LA Level Emigration Estimates (3 Year Average) Predictor Variable 1 Variable 2 Variable 4 Variable 3 8

9. ABDCEABCDE New Migration Geographies (NMGos) 9 REGION Z NMGO 1NMGO 2 ABDCE Local Authority NMGo Region Cluster Analysis

10. Constraining Emigration Estimates REGION Z NMGO 1NMGO 2 10,5007,5003,0004,0002,000 ABDCE Local Authority NMGo Region 6,00021,000 Predicted Estimates Final Estimates 10,0007,1432,8573,3331,667 20,0005,000 25,000 Emigrants 10

11. Work Carried Out So Far 11 Updating the Emigration Methodology Investigate a Non-Modelling Approach Update the Current Method

12. Explored a non-modelling approach Greater use of administrative data sources Closely similar to the immigration method o BUT unlike the immigration method, there are no datasets which directly count emigration at Local Authority level The Non-Modelling Approach 12

13. Non-Modelling Approach: Streaming Migrants IPS England and Wales National Emigration Estimate Reason for Migration Study E.g.  Higher Education Statistics Agency (HESA) Student Record Work E.g.  Lifetime Labour Market Database (L2) Other Children17-5960 + E.g.  Patient Register Data System (PRDS) 13

14. Lifetime Labour Market Database (L2) o 1% sample of records on the National Insurance and Pay as You Earn System (NPS) o Economic activity o 12 months or more of economic inactivity as an indicator of possible emigration IPS ‘Other’ Category o Out-migrants coded as ‘Other’ when free-text answer related to ‘Work’ or ‘Study’ reason Non-Modelling Approach: Data sources 14

15. Promising results, however... o Improvements in data sources needed o Timing considerations need more development Research and results will help inform how we update the current emigration model Source: Emigration user update August 2014, ONS Non-Modelling Approach: On pause 15

16. Updating the Current Model Part 1: Removal of NMGos Part 2: Preliminary Results Part 3: Investigating Predictor Variables 16

17. Remove the Intermediate Geography – New Migration Geography (NMGo) Local Authority population size used in the model to account for area differences (as a rate) Constraining to the IPS- based region level estimates Intermediate (NMGo) Local Authority Regional Part 1: Removing NMGos 17

18. Reviewed: Research Review Group (ONS Panel of Experts) Consultation with University of Southampton Approved: Removing NMGos Poisson regression method Using LA population size to account for area differences Part 1: Removing NMGos 18

19. Part 2: Preliminary Results Removing the NMGos 19

20. Without NMGo Model & Current Model 20 Birmingham Wandsworth Newham Tower Hamlets Camden Manchester Ealing Brent Haringey Lambeth Lewisham Hackney Southwark Richmond upon Thames Kensington & Chelsea Westminster Hammersmith & Fulham City of London Cardiff Oxford Leeds

21. Birmingham Wandsworth Newham Tower Hamlets Camden Manchester Ealing Brent Haringey Lambeth Lewisham Hackney Southwark Richmond upon Thames Kensington & Chelsea Westminster Hammersmith & Fulham City of London Cardiff Oxford Leeds IPS LA Outflows & Current Model 21 City of Bristol Stafford

22. Birmingham Wandsworth Newham Tower Hamlets Camden Manchester Ealing Brent Haringey Lambeth Lewisham Hackney Southwark Richmond upon Thames Kensington & Chelsea Westminster Hammersmith & Fulham City of London Cardiff Oxford Leeds IPS LA Outflows & Without NMGo Model 22 City of Bristol Stafford

23. IPS Outflow – London Region (2012): 102,683 Case Study: London NMGos ModelNMGoPredictedConstrained Current ModelLOI126,50225,351 LOI226,62325,510 LOI324,25323,133 LOI417,13416,404 LOI512,94012,286 Without NMGo Model LOI126,21325,047 LOI223,16422,134 LOI320,29619,396 LOI427,36726,153 LOI510,4149,952 23

24. NMGo Case Study: LOI3 (Predicted) 24

25. NMGo Case Study: LOI3 (Final) 25

26. NMGo Case Study: LOI4 (Predicted) 26

27. NMGo Case Study: LOI4 (Final) 27

28. Part 3: Investigating Predictor Variables 28

29. Project is currently researching potential predictor variables:  Less reliance on Census data  Greater use of administrative data sources  More intuitive  Using ‘manual selection’ of predictor variables  Data sources investigated thoroughly at LA level Part 3: Investigating Predictor Variables 29

30. University of Southampton consultations approve: Stepwise and manual selection of predictor variables But ensure no correlation between predictor variables and LA population size Part 3: Investigating Predictor Variables 30

31. Data sources which are being explored include: Patient Register Database HESA Student Record HESA Destination of Leavers Survey Lifetime Labour Market Database (L2) Migrant Worker Scan English School Census Welsh School Census Annual Population Survey Home Office Crime Statistics Part 3: Investigating Predictor Variables 31

32. Population aged 64 and over Students (aged 20 to 25) of Non-UK nationality in their final year of study Students (aged 20 to 25) of Non-EU nationality in their final year of study Students of Non-EU nationality in their final year of study In-migrants of EU8 nationality registering for a National Insurance number Employed individuals, aged 16 and over Long-term international in-migration flows Short-term international in-migration flows Household with accommodation owned outright Household with accommodation owned with a mortgage Higher/further education students of non-UK nationality Part 3: Possible Predictor Variables 32

33. Next Steps 1)Continuing research into updating the method 2)Assessment and comparisons 3)Review of changes by RRG and University of Southampton 4)Further consultations with users 33

34. Key Points  Non-modelling approach on pause  Emigration method updated: 1)Removing NMGos 2)Updating Predictor Variables  Impacts of updates are being investigated 34

35. What other local data sources should we be exploring? What are your thoughts on the ‘manual selection’ of predictor variables? 35

36. Thank You for Listening psru@ons.gsi.gov.uk 36