Modelling international migration to produce local level estimates

Ruth Fulton

Office for National Statistics

Outline of presentation

Context

Immigration

– Current method

– Using administrative data

– Modelling approach

Data sources, Fitting the model,

Diagnostics/ validation, Impact on estimates

Emigration

Context of work

• Improving Migration and Population Statistics (IMPS)

• Previous improvements to immigration and emigration methodology (2007)

• Forthcoming package of improvements

(May 2010)

The importance of international migration

• Key driver of population change

UK Components of Change, mid-1991 to mid-2007

-50

0

50

100

150

200

250

300

1991

-199

2

1992

-199

3

1993

-199

4

1994

-199

5

1995

-199

6

1996

-199

7

1997

-199

8

1998

-199

9

1999

-200

0

2000

-200

1

2001

-200

2

2002

-200

3

2003

-200

4

2004

-200

5

2005

-200

6

2006

-200

7

Th

ou

san

ds

Natural change Net migration & other changes

Current method: immigration

• National level– International Passenger Survey (IPS) data only

• Government Office Region (GOR) & Wales level– IPS data calibrated to Labour Force Survey (LFS) data– LFS data averaged over three years

• Intermediate geography level– IPS data averaged over three years

• Local authority level – 2001 Census data

Current method: issue

• Current method uses 2001 Census data to

distribute to LA level

• Clear changes in migration trends since 2001

e.g. EU accession

• Bias introduced to LA estimates where Census

distribution has changed

Improving the current method:

0

500

1000

1500

2000

2500

3000

3500

2001 2002 2003 2004 2005 2006

Peterborough existing Peterborough new method

Fenland existing Fenland new method

Huntingdonshire existing Huntingdonshire new method

Use of administrative data

• Potential use of administrative data:

• GP registrations (Flag 4s)

• National Insurance Number (NINo) allocations to

overseas nationals

• Improves timeliness at LA level

• Differences in coverage and definitions

Comparison of Flag 4s and NINos

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000

NINo

Fla

g 4

Birmingham

Brent

Westminster

Tow er Hamlets

Hackney

Herefordshire

Canterbury

Sheffield

Nottingham

Oxford

(x=y)

A modelling approach

• Produces estimates for LAs(IPS data cannot be used directly at this level)

• ‘Borrows strength’ from other data sources (covariates)

• Model fitted at the LA level describing relationship between IPS and covariates

• Fitted model can be used to obtain LA estimates

Model specification

)(~ˆ jj PY

βx jj )log(

where

jY

j

jx

= direct IPS estimate, no. immigrants going to LADj

= expected total count of immigrants, LADj

= set of covariates for LADj

Alternative approaches

• Modelling IPS sample counts

• Modelling IPS sample counts, with average LA weight as offset OR additional covariate

• Scaling IPS direct estimate to a count scale (or standardising IPS sample count)

• Fitting model at NMGi level, estimating coefficients and applying model at LA level

Variables entered for potential selection

NINos

Country of Birth

EthnicPopulation

Flag 4sUK-born

ImmigrantsPopulation

Density

Foreign Armed Forces

Industry

Mid-yearPop Est

ForeignStudents

Job CentreVacancies

Home Armed Forces

InternalMigration

UnempEstimates

Fixed covariates currently in the model

NINoS

Country of Birth

EthnicPopulation

Flag 4sUK-born

ImmigrantsPopulation

Density

Foreign Armed Forces

Industry

Mid-yearPop Est

ForeignStudents

Job CentreVacancies

Home Armed Forces

InternalMigration

UnempEstimates

Diagnostic and Validation tests

• Model diagnostics

Pseudo R2

Residual plots

Model vs Sample estimate plots

• Comparing the 2001 model based estimates with the 2001 Census data

• Comparing the sum of the model based estimates for LAs with the NMGi estimate

• Checking the time-series

Time series check

0

500

1000

1500

2000

2500

3000

2001 2002 2003 2004 2005 2006

West Wiltshire Existing method Flow(LA) Flow(NMGi)

Model vs Sample Estimate plot (04/05)

0

5000

10000

15000

20000

25000

0 5000 10000 15000 20000

Model Based Estimates (Unconstrained)

Dir

ect

Su

rvey

Est

imat

es

04/05

Existing New

Preliminary Impacts Assessment

0102 0203 0304 0405 0506

≥ 1000 6 6 11 20 13

500 to 999 18 12 24 24 20

100 to 499 62 59 55 45 54

-99 to 99 181 199 140 108 170

-100 to -499 96 87 128 149 99

-500 to -999 9 10 12 25 13

≤ -1000 4 3 6 5 7

Current methods: emigration

• National level• International Passenger Survey (IPS) data only

• Government Office Region (GOR) & Wales level• IPS data only

• Intermediate geography level• IPS data averaged over three years

• Local authority level • Model based distribution (propensity to migrate)

Improvements

• Fits model at local authority level rather than

intermediate geography level

• Uses Poisson modelling and models number of

migrants rather than propensity to migrate

• Tested some additional covariates, e.g. more

detailed ethnic group and fixes covariates

Impact of model based distribution

• Only affects the distribution of number of immigrants and emigrants within the intermediate geography

• Migration estimates for local authorities will change for mid-2002 to mid-2008 as a result

Further Information

• Quarterly updates and other information at www.statistics.gov.uk/imps

• Email:

imps@ons.gsi.gov.uk

ruth.fulton@ons.gov.uk

• Consultation papers (December 09)