Improving the estimation of long-term international emigration at local authority level

Joshua Turner

Population Statistics Research Unit (PSRU)

Local Insight Reference Panels

1

Session Topics

Brief Background

Work so far

Preliminary results: Impact of changes

Current work

Next steps

2

Brief Background

3

Importance of Emigration

• Birmingham

Source: Population Estimates for UK, England and Wales, Scotland and Northern Ireland, Mid-2011 and Mid-2012, ONS

Mid-2011 Population 1,074,283

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 Population 1,085,417

4

Brief Background

• No datasets with robust counts of emigration at Local Authority level

• Current method uses a model-based approach

5

Stepwise Model

Stepwise modelStepwise model

Relationship between

Relationship between

IPS LA Level

Emigration Estimates

(3 Year Average)

Predictor Variable 1Predictor Variable 1

Variable 2Variable 2

Variable iVariable i

Variable 3Variable 3

Variable 4Variable 4

Variable 5Variable 5

Predictor Variable 1Predictor Variable 1

Variable 2Variable 2

Variable 4Variable 4

Variable 3Variable 3

6

Predictor Variables

7

Poisson Regression Model

Emigration estimates

constrained to IPS totals

Poisson Regression

Model

IPS LA Level

Emigration Estimates (3

Year Average)

Predictor Variable 1Predictor Variable 1

Variable 2Variable 2

Variable 4Variable 4

Variable 3Variable 3

8

A B D C E

New Migration Geographies (NMGos)

9

REGION Z

NMGO 1 NMGO 2

Local Authority

NMGo

Region

Cluster Analysis

Constraining Emigration Estimates

REGION Z

NMGO 1 NMGO 2

Local Authority

NMGo

Region

6,00021,000Predicted Estimates

Final Estimates

20,000 5,000

25,000 Emigrants

10

Work Carried Out So Far

11

Investigate a Non-Modelling

Approach

Update the Current Method

• 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

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

Children 17-59 60 +

E.g.Patient Register Data System (PRDS)

13

• Lifetime Labour Market Database (L2)o 1% sample of records on the National Insurance

and Pay as You Earn System (NPS)o Economic activityo 12 months or more of economic inactivity as an

indicator of possible emigration

• IPS ‘Other’ Categoryo Out-migrants coded as ‘Other’ when free-text

answer related to ‘Work’ or ‘Study’ reason

Non-Modelling Approach: Data sources

14

• Promising results, however...o Improvements in data sources neededo 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

Updating the Current Model

• Part 1: Removal of NMGos• Part 2: Preliminary Results• Part 3: Investigating Predictor Variables

16

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

Reviewed:• Research Review Group (ONS Panel of

Experts)• Consultation with University of Southampton

Approved:Removing NMGosPoisson regression methodUsing LA population size to account for area

differences

Part 1: Removing NMGos

18

Part 2: Preliminary Results

Removing the NMGos

19

Without NMGo Model & Current Model

20

Birmingham

Wandsworth

Newham

Tower Hamlets

Camden

Manchester

EalingBrent

Haringey

Lambeth

Lewisham

Hackney

Southwark

Richmond upon Thames

Kensington & Chelsea

Westminster

Hammersmith & Fulham

City of London

Cardiff

Oxford

Leeds

Birmingham

Wandsworth

Newham

Tower Hamlets

Camden

Manchester

Ealing

Brent

Haringey

LambethLewisham

Hackney

Southwark

Richmond upon Thames

Kensington & Chelsea

Westminster

Hammersmith & Fulham

City of London

Cardiff

Oxford

Leeds

IPS LA Outflows & Current Model

2121

City of Bristol

Stafford

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

IPS Outflow – London Region (2012): 102,683

Case Study: London NMGos

Model NMGo Predicted Constrained

Current Model LOI1 26,502 25,351

LOI2 26,623 25,510

LOI3 24,253 23,133

LOI4 17,134 16,404

LOI5 12,940 12,286

Without NMGo Model

LOI1 26,213 25,047

LOI2 23,164 22,134

LOI3 20,296 19,396

LOI4 27,367 26,153

LOI5 10,414 9,952

23

NMGo Case Study: LOI3 (Predicted)

24

NMGo Case Study: LOI3 (Final)

25

NMGo Case Study: LOI4 (Predicted)

26

NMGo Case Study: LOI4 (Final)

27

Part 3: Investigating Predictor Variables

28

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

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

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

• 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

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

Key Points

Non-modelling approach on pause Emigration method updated:

1) Removing NMGos

2) Updating Predictor Variables

Impacts of updates are being investigated

34

What other local data sources should we be exploring?

What are your thoughts on the ‘manual selection’ of predictor variables?

35

Thank You for Listening

psru@ons.gsi.gov.uk

36