Improved Method for the Geographical Distribution of

Out-Migrants

Fiona Aitchison and Jonathan Swan

Former Method

England and Wales

GOR/Wales

Local Authorities

Intermediate Geography: HA/FHSAs

National

Published IPS

3 Year IPS average used to apportion GOR

North East

Newcastle &

North Tyneside

Newcastle upon Tyne North Tyneside

Geographic Level Data/Methods

Previous year’s resident population used to apportion

HA/FHSAs

New Method

National

GOR/Wales

LAs

Propensity to Migrate model

used to apportion NMGo

3 Year IPS average used to apportion GOR

Published IPS

New Migration Geography for emigrants (NMGo)

Newcastle upon Tyne North Tyneside 5 Other LAs

NEI1

North East

England and Wales

Geographic Level Data/Methods

(Including new visitor switcher assumptions)

Factors available in the model

• Armed Forces• Crime• Education• Employment• Ethnicity• Housing• Deprivation• Migration• Existing population• Socio-economic classification• Students• Tenure• Country of Birth

Modelling methods considered

• Factor Analysis followed by Enter method Linear Regression

– Created 4 or 5 components built from approximately 20 of the available 100+ variables.

– Model gave an R2 value of approximately 68%– Disadvantage: Complex with hard to interpret results

• Forward-Stepwise Linear Regression– Created model with 3 variables selected from the

available 100+ – Model gave an R2 value of approximately 78%

Modelling methods considered

• Forward-Stepwise Linear Regression with logged variables

– Model gave an R2 value of approximately 75%– Disadvantage: A number of variables could not have

logarithm taken

• Forward-Stepwise Linear Regression (direct count of out-migrants)

Testing procedure

• Precision of model measured using the Average Square Error (ASE) on a number of test sets of data

• Log model was found to be subject to bias towards underestimation

Modelling Method Indicative ASE

Factor Analysis 0.45

Stepwise Regression (Propensity) 0.26

Stepwise Regression (Logs) 0.28

• The stepwise regression model of propensity to migrate was selected due to more plausible results

Example of the model: 2006

• In 2006 the variables below are used to form the model, in addition to a constant term.

• Estimated in-migrants• Males aged 16-34 with limiting long-term illness• Persons in higher professional occupations• Females aged 40-44• Percentage of males in population

• Model results in a significant improvement – The percentage of variance explained is increased– R2 increases from around 40% to over 80%– In 2006 R2 is 91%

Changes from Indicative Results

• Indicative results for revised 2002 to 2005 estimates were published in April 2007

• An additional variable, Country of Birth, was included in the list of factors

• The intermediate geography was revised for the West Midlands and Wales

• The models for these years have all changed slightly in terms of the variables selected

Future Work

• It is not intended to change the modelling methodology for at least the next two years

– The model will still be updated each year with new data– Results from extra out-migrant filter shifts on IPS will

become available

• Further research in this area will be taken forward as part of wider migration research

International Migration Sex Ratios

Sex Ratio – Methodology Considered

• Group LAs into quartiles and/or quintiles• In Migrants

– Grouped by sex ratio of Census one year ago resident outside UK

• Out Migrants– Grouped by sex ratio of resident LA population.– Groups fixed by 2001 ratios and– Variable groups by previous years population

considered.

• Research undertaken by Michelle Littlefield, ONSCD

Sex Ratio – example grouping Out migrants, quintiles, variable membership

0.6

0.8

1

1.2

1.4

1.6

2001 2002 2003 2004 2005

1

2

3

4

5

Quintile

Sex ratios – Out MigrantsLondon vs. Non London

0.6

0.8

1

1.2

1.4

1.6

2001 2002 2003 2004 2005

Out of London London

Sex ratios - Conclusions

• All the variants we examined for LA groupings produced broadly similar results.

• Therefore, not able to determine stable groupings of LAs for sex ratios.

• London / non-London split produced results we were not able to explain.

• Therefore unable to produce method for sex-ratios of international migrants.

• So the national sex-ratio is used.• Subject of possible further research.

International Out Migration Age Distribution

International Migration (IPS) age profiles

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70+

Age

Pro

po

rtio

n o

f to

tal m

igra

nts

in a

ge

gro

up

non-British out-migrants

non British - in-migrants agedon 2 years

Allocation of British / Non British

0%

10%

20%

30%

40%

50%

60%

70%

Mid-02 Mid-03 Mid-04 Mid-05Mid-year

% n

on

-Bri

tish

IPS

co

nta

cts

1 2 3 4 5 2-4

Age Distribution of British out-migrantsGrouping LAs (Males)

0

1

2

3

4

5

6

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Age

Ou

t-m

igra

nts

%

Cluster 1 Cluster 2

Summary of Age Distribution Approach

• For each sex separately• Split into British non-British

– LAs grouped by quintiles – middle three grouped– Quintiles on in-migrants as % of resident population

• Non British– Use age individual LA distribution of in-migrants …– but aged on two years

• British– Split into two clusters– Clusters based on resident population age distribution– Use IPS quintile age distribution– Split to SYOA based on Census in-migrant distribution

• Research undertaken by Karen Gask

Any Questions…