Transcript ONS 2011 migration session presentations

An Improved Method for Estimating
Immigration to Local Authorities in
England and Wales
Nigel Swier
British Society of Population Studies (BSPS) Conference
York, 7-9 September 2011
Overview
• What is being improved?
• Issues with producing sub-national
immigration estimates
• Previous approaches
• Outline of new method
• How is this new method an improvement?
• Results (to be released on 17 November)
Mid-Year Population Estimates (MYEs):
Cohort Component of Change
Previous Year Resident Population
“Age On”
Add Natural Change (Births – Deaths)
Add Net Internal Migration (Inflows – Outflows)
Add Net International Migration
(Immigration – Emigration)
Estimating Immigration
• UN definition of long-term international
migrant: “12 months or more”
• Based mainly on International Passenger
Survey (IPS)
• Adjustments for:
- Visitor/migrant ‘switchers’
- Asylum seekers
Using IPS to estimate immigration
1. National level IPS estimates are robust
2. Can not produce LA level estimates directly
3. Centralizing tendency
Evolution of LA Distribution Methodology
Previous (2007)
Current (2010)
Improved
England and Wales Immigration Estimate (IPS)
Region/ • Uses distributions from admin
Distribute
using 3 yr LFS average
data not
counts.
Country
• Original concept developed by
University of Leeds
Intermediate
3 yrs of
IPS access
data
• EnhancedUse
through
ONS
to microdata:
Geography
- Refining
definitionsModel-based
2001 Census
distribution
distribution
- Minimizing double counting
• Doesn’t address
weaknesses with other
Split by Reason for
parts of the methodology
Migration:
• Modelling
lacks
Work,approach
Study,
transparency
Returning, Other
• Results are difficult to
explain for some LAs
Distribute using most
appropriate admin source
Local Authority Immigration Estimates
New Distributional Model Overview
IPS England and Wales Estimate (plus switcher adjustments)
Worker
stream
Student
stream
Migrant
Workers
Scan
HESA
(Higher
Education)
Returning Migrants
UK Born
Non-UK Born
BIS/WG
(Further
Education)
Other stream
Children 17-59
GP Patient
Register:
‘Flag 4s’
60+
Asylum
Seekers
(NASS)
L2
Residual
LTM
Worker
distribution
STM Student
Distribution/
estimate
LTM
Student
distribution
Local Authority Immigration (LTIM) estimates
2001
Census
distribution
How is this method an improvement?
• More transparent
• Uses more timely data
• Removes known biases
• More accurate
LA correlations of immigration with APS
foreign born (as % of England & Wales total)
1.000
0.950
0.900
0.850
0.800
Coefficient of
0.750
Determination (R 2)
0.700
0.650
0.600
Current Method
Improved Method
0.550
0.500
2006
2007
2008
2009
2010
Next Steps
• Release on 17 November:
- Research paper explaining methods
- Indicative immigration (and emigration) estimates
- Indicative population estimates
- Impact assessment
• No revisions before 2011 Census results
• ONS intend to incorporate method into 2010base SNPPs
Questions?
Distributing immigrant IPS flows:
workers (first arrivals)
Simon Whitworth (ONS)
Jennifer Ford-Evans (ONS)
Andrew Needham (DWP)
Outline
•
•
•
•
•
•
Recap of method
Use of the International Passenger Survey
Use of admin sources
Basic methodology
Additions to the methodology
Summary
Who are we distributing?
International Passenger Survey Estimate (plus switcher adjustments)
Worker
flow
Student flow
Returning Migrants
UK Born
Non-UK Born
Other flow
Children 17-59
60+
Total long and short term
migration
Migrant
Workers
Scan
HESA
(Public
Higher
Education)
BIS/WAG
Data (ESOL/,
Further
Education)
LTM
Worker
distribution
STM Student
Distribution/
estimate
Flag 4: GP
Patient
Register
Asylum
Seekers
(NASS)
L2
Residual
LTIM LA estimate
LTM
Student
distribution
2001
Census
distribution
Who are we distributing?
All the long-term migrant
workers in E&W each year.
This total number is taken from
the IPS.
Identifying LT migrant workers on the IPS
Is the country of residence 12 months
ago outside the UK?
YES
NO
Not a Migrant
Identifying LT migrant workers on the IPS
Is the country of residence 12 months
ago outside the UK?
NO
Not a Migrant
NO
Short-term
migrant or visitor
YES
Is the migrant planning on staying in
the UK at least 12 months?
YES
Identifying LT migrant workers on the IPS
Is the country of residence 12 months
ago outside the UK?
NO
Not a Migrant
NO
Short-term
migrant
YES
Returning migrant
YES
Is the migrant planning on staying in
the UK at least 12 months?
YES
Has the migrant lived in UK before?
NO
Identifying LT migrant workers on the IPS
Is the country of residence 12 months
ago outside the UK?
NO
Not a Migrant
NO
Short-term
migrant
YES
Returning migrant
NO
Not a long-term
worker
YES
Is the migrant planning on staying in
the UK at least 12 months?
YES
Has the migrant lived in UK before?
NO
Is the migrant aged 16+?
YES
Identifying LT migrant workers on the IPS
Is the country of residence 12 months
ago outside the UK?
NO
Not a Migrant
NO
Short-term
migrant
YES
Returning migrant
NO
Not a long-term
worker
NO
Not a long-term
worker
YES
Is the migrant planning on staying in
the UK at least 12 months?
YES
Has the migrant lived in UK before?
NO
Is the migrant aged 16+?
YES
Is the intended Reason for Visit one
of?
• Definite job to go to
• Looking for work
• Working Holiday
• Business
• Other but previous occupation not
retired or houseperson
• Accompanying/joining and
previous occupation work
YES
Identifying LT migrant workers on the IPS
Is the country of residence 12 months
ago outside the UK?
NO
Not a Migrant
NO
Short-term
migrant
YES
Returning migrant
NO
Not a long-term
worker
NO
Not a long-term
worker
YES
Is the migrant planning on staying in
the UK at least 12 months?
YES
Has the migrant lived in UK before?
NO
Is the migrant aged 16+?
YES
Is the intended Reason for Visit one
of?
• Definite job to go to
• Looking for work
• Working Holiday
• Business
• Other but previous occupation not
retired or houseperson
• Accompanying/joining and
previous occupation work
YES
LONG-TERM
MIGRANT
WORKER
What we are using to distribute
• The Lifetime Labour Market Database (L2)
and the Migrant Workers Scan (MWS)
• Derived from the National Insurance and
PAYE System (NPS) - a database with
records of all National Insurance Number
(NINo) registrations since 1975.
What the MWS is
• The MWS is a subset of the NPS and
contains information on all the overseas
nationals in the NPS
• It excludes any data about activities
• Address at registration is used as proxy for
address at arrival.
• It includes both long-term and short-term
migrants with no way to distinguish.
How is the MWS used in the methodology
The MWS is used to get
the total count of migrant NINo registrations
in an LA.
What the L2 is
• The L2 is a 1% extract of data from the NPS.
• This 1% contains over 750,000 individuals.
• The L2 holds detailed records for each tax
year from 1975 including activity information.
• Each record can have various data present.
How the L2 is used in the methodology
The L2 is used to calculate
the proportion of LT migrant workers in an
LA.
Definitions and assumptions on the L2
• Address at registration is used to allocate a
geography to a migrant.
• Activity denotes residency
• Ceasing activity denotes departure
• Re-engaging with activity denotes re-arrivals
Types of LT migrants
Identifying LT migrant workers on the L2
Is the country of origin outside UK
and are they coming from abroad?
YES (then migrant)
NO
Not a Migrant
Identifying LT migrant workers on the L2
Is the country of origin outside UK
and are they coming from abroad?
NO
Not a Migrant
YES (then migrant)
What is the inferred length of stay?
Greater than12 months
(then long-term)
Less than
12
months
Short-term migrant
Identifying LT migrant workers on the L2
Is the country of origin outside UK
and are they coming from abroad?
NO
Not a Migrant
YES (then migrant)
What is the inferred length of stay?
Greater than12 months
(then long-term)
Does the LT migrant have an
employment/self-employment record,
JSA record or Working Tax Credit?
YES
Less than
12
months
NO
Short-term migrant
Not a long-term
worker
Identifying LT migrant workers on the L2
Is the country of origin outside UK
and are they coming from abroad?
NO
Not a Migrant
YES (then migrant)
What is the inferred length of stay?
Greater than12 months
(then long-term)
Does the LT migrant have an
employment/self-employment record,
JSA record or Working Tax Credit?
Less than
12
months
Short-term migrant
NO
Not a long-term
worker
NO
Not a long-term
worker
YES
Are there 6 months or less between
arrival and registration?
YES
Identifying LT migrant workers on the L2
Is the country of origin outside UK
and are they coming from abroad?
NO
Not a Migrant
YES (then migrant)
What is the inferred length of stay?
Greater than12 months
(then long-term)
Does the LT migrant have an
employment/self-employment record,
JSA record or Working Tax Credit?
Less than
12
months
Short-term migrant
NO
Not a long-term
worker
NO
Not a long-term
worker
YES
LT worker who is
also a student
(Taken out to
eliminate
duplication
between streams)
YES
Are there 6 months or less between
arrival and registration?
YES
Is the LT migrant worker a “potential
student”?
NO
Identifying LT migrant workers on the L2
Is the country of origin outside UK
and are they coming from abroad?
NO
Not a Migrant
YES (then migrant)
What is the inferred length of stay?
Greater than12 months
(then long-term)
Does the LT migrant have an
employment/self-employment record,
JSA record or Working Tax Credit?
Less than
12
months
Short-term migrant
NO
Not a long-term
worker
NO
Not a long-term
worker
YES
LT worker who is
also a student
(Taken out to
eliminate
duplication
between streams)
YES
Are there 6 months or less between
arrival and registration?
YES
Is the LT migrant worker a “potential
student”?
NO
LONG-TERM MIGRANT
WORKER
Recap of methodology
LT worker figure for LA “X”
=
MWS count in LA “X”
x
L2 proportion of LT workers in LA “X”
Recap of methodology
International Passenger Survey Estimate (plus switcher adjustments)
Worker
flow
Student flow
Returning Migrants
UK Born
Non-UK Born
Other flow
Children 17-59
60+
Total long and short term
migration
Migrant
Workers
Scan
HESA
(Public
Higher
Education)
BIS/WAG
Data (ESOL/,
Further
Education)
LTM
Worker
distribution
STM Student
Distribution/
estimate
Flag 4: GP
Patient
Register
Asylum
Seekers
(NASS)
L2
Residual
LTIM LA estimate
LTM
Student
distribution
2001
Census
distribution
Sample size issues
Using the method just described, the 1% L2
sample size means that in some LAs, the
proportion of workers is very variable over time.
In these cases, the proportion could not be
used directly at LA level.
Sample size issues
• To compensate for this, we have applied the
proportion of LT workers at a higher
geography to those LAs that were deemed
not sufficiently robust.
• The proportion is applied to the MWS
o Directly at LA level in the case of 59 LAs.
o At NUTS 3 geography in the case of 203 LAs.
o At NUTS 2 geography in the case of 114 LAs.
Sample size issues
NUTS 2 in East of England:
East Anglia
Essex
Bedfordshire and Hertfordshire
NUTS 3 in East of England
East Anglia
Cambridgeshire
Peterborough
Norfolk
Suffolk
Essex
Essex CC
Thurrock
Southend-on-sea
Bedfordshire and Hertfordshire
Luton
Bedfordshire CC
Hertfordshire
Sample size issues
In the East of England,
there are:
2 LAs which use the
proportion at NUTS 2 level
42 which use the proportion
of LT workers at NUTS 3
level
4 which use the proportion
of LT workers directly at LA
level
Reconciling IPS and L2
• The two groups of LT migrant workers (IPS
and L2) were compared
• Some differences in the country of origin
breakdowns were discovered
Reconciling IPS and L2
Accounting for this, a “subcontinent weighting
factor” was introduced based on the E&W
subcontinent split.
L2
IPS
EU
EU
Asia
Asia
Aus/NZ
Aus
/NZ
RoW
RoW
LT
workers
LT
workers
Summary of methodology
Calculate LT worker proportion in the L2 and apply to MWS count
Summary of methodology
Calculate LT worker proportion in the L2 and apply to MWS count
LT worker figure in each LA from L2 and MWS
Summary of methodology
Calculate LT worker proportion in the L2 and apply to MWS count
Split into subcontinents
Aus
EU figure in each LAAsia
RoW
LT worker
from L2 and
MWS
/NZ
Summary of methodology
Calculate LT worker proportion in the L2 and apply to MWS count
Split into subcontinents
Calculate weighting factor from IPS subcontinent split and weight L2 based
data
L2/MWS E&W Before
Aus
EU figure in each LAAsia
RoW
LT worker
from L2 and
MWS
/NZ
Summary of methodology
Calculate LT worker proportion in the L2 and apply to MWS count
Split into subcontinents
Calculate weighting factor from IPS subcontinent split and weight L2 based
data
L2/MWS E&W After
EU
Asia LA from
Aus/NZ
RoW
LT worker
figure in each
L2 and MWS
Summary of methodology
Calculate LT worker proportion in the L2 and apply to MWS count
Split into subcontinents
Calculate weighting factor from IPS subcontinent split and weight L2 based
data
Distribute each IPS subcontinent total using weighted L2/MWS
distributions and sum together to get total LT worker counts by LA
Aus
EU EUin
Asia
Aus/NZ
RoW
RoW
Asia
LTLTworkers
worker
figure
eachinLA
each
thatLA
sum
from
to L2
IPSand
E&W
MWS
total
/NZ
Any questions?
?
Using administrative data to distribute
long-term international immigrant students
to Local Authority (first arrivals)
Helena Howarth and Sofie De Broe
BSPS Conference (York): 7-9 September 2011
Overview of Presentation
1. Defining students in the IPS
2. Defining the student subgroups
a. Group 1 – Higher Education Students at
Government Funded Institutions
a. Group 2 – Higher Education Students at
Privately Funded Institutions
a. Group 3 – Further Education Students
3. Distributing students to Local Authority Level
New Distributional Model Overview
International Passenger Survey Estimate (plus switcher adjustments)
Worker flow
Student
flow
Returning Migrants
UK Born
Non-UK Born
Other flow
Children 17-59
60+
Total long and short term
migration
L2
HESA
(Higher
Education)
BIS/WG
(Further
Education)
Flag 4: GP
Patient
Register
Asylum
Seekers
(NASS)
Migrant
Worker
Scan
STM Worker
Distribution/
estimate
LTM
Worker
distribution
STM Student
Distribution/
estimate
LTM
Student
distribution
LTIM LA estimate
2001
Census
distribution
Relative sizes of groups
100%
80%
Other
60%
Returning
Migrants
40%
Students
20%
Workers
0%
mid2006
mid2007
mid2008
mid2009
Year of IPS data
mid2010
Estimates for mid-2006
to mid-2010 show that
students have become
the largest group.
Identifying LT migrant students on the IPS
Is the country of residence 12 months
ago outside the UK?
NO
Not a Migrant
NO
Short-term
migrant
YES
Returning
migrant
NO
Not a longterm student
NO
Not a longterm student
YES
Is the migrant planning on staying in
the UK at least 12 months?
YES
Has the migrant lived in the UK before?
NO
Is the migrant aged 17-59?
YES
Is the intended Reason for Visit
‘Formal Study’
YES
LONG-TERM MIGRANT
STUDENT
Student groups
1. “Higher education (HE) students are those students on
programmes of study for which the level of instruction is above that
of level 3 of the National Qualifications Framework” (HESA)
• Proportions HE/FE from 2004 and 2005 IPS data
2. HE publicly-funded versus HE private or for-profit institutions
• Proportions from a Home Office publication on Visa compliance and
comparing counts in the HESA student record versus IPS estimates.
HE
Government
20%
HE Private
12%
68%
Further
Education
Reason for distributing HE and FE separately
5 Welsh LA’s:
•
6 FE institutions
•
5 HE institutions
•
0 private
institutions
HE Government - Processing
(1)
Sub-setting
HESA data
(2)
(3)
Linking to
other
administrative
sources
Imputation
of missing
term-time
addresses
HE Government – Sub-setting
Group Removed
Reason
Domicile = UK
Not migrants
Year >= 2
Not in their first year of study therefore a
different migrant inflow
Campus = Abroad
They have not migrated to the UK
Previous institution
in UK
Not their first UK institution therefore a
different migrant inflow or in ‘returning migrant’
stream
Start date to End
date < 365 days
Short-term migrant
Age <= 16 or >=60
Children and older people are distributed
separately as part of the ‘other’ stream
Mode = Distance
learning
Assumed that individuals would not migrate to
distance learn
HE Government - Linkage
Linked using:
1) Student Identifier
2) 3 letters of
forename, 3
letters of
surname, sex and
date of birth
HESA Student Record
– Previous Year
Linked using:
HESA Student Record
– Current Year
1) Date of birth, sex
and term-time
postcode
2) Repeat for all
available
postcodes
Migrant Workers Scan
– Quarterly data to present
HE Government - Imputation
•
Term-time address is sometimes missing. HESA is
actively working with universities to decrease the
proportion missing.
•
Migrant subset in:
•
The percentage missing varies by institution
2007/08 ~ 22% missing
2009/10 ~ 13% missing
Method of imputation:
•
•
Find LA level distribution of students with term-time
address by campus
Apply this distribution to those with no term-time
address by campus
HE Private
• HESA conducted a census of Private Providers of HE
education in 2010.
• This asked for aggregate data by:
1) mode of study
2) level of study
3) domicile
4) subject
• Institution address used to allocate students:
o Outside London – Term-time address distribution of HESA
government institutions in the LA where available and LA of
institution where not.
o London – Term-time address distribution of all HESA
government institutions in the London region
Further Education
Datasets:
• Individualised Learner Record (BIS)
• Lifelong Learning Wales Record (Welsh
Government)
Key Points:
• Domicile known
• Term-time address
• No length of stay data
• Coverage based on funding
Combining the Data – Region
HE
Government
HE Private
Yo
r
ks
th
e
hi
re
&
N
or
th
th
or
N
Ea
Lo st
So nd
ut on
So h E
ut as
t
h
W
es
W t
al
es
t
W
e
Ea Hu st
st m
b
W Mi er
es dla
tM n
d
id s
la
nd
s
Further
Education
Ea
s
Percent
70
60
50
40
30
20
10
0
Region
Note – The figures used in this example are as closely allied to the data we have used
as available from published sources but do not represent our final results.
Combining the data - Method
Take the proportions of all students in each group:
• HE Government = 0.68
• HE Private = 0.12
• FE = 0.20
Apply these proportions to the distributions produced for each
region:
e.g. London
•
•
•
•
HE Government = 27.7%
HE private = 60.5%
FE = 12.4%
0.68*27.7 + 0.12*60.5 + 0.20*12.4 = 28.6%
Apply this proportion to the total number of migrant students:
e.g. London in 2009/10
0.286*195k = 56k
Yo
rk
sh
i
No
rth
No Ea
re
rth st
&
W
th
es
e
t
Hu
Ea
st mb
er
M
i
W
dl
es
a
t M nds
id
la
nd
s
Ea
st
Lo
nd
So
on
ut
h
Ea
So
st
ut
h
W
es
t
W
al
es
Count (000's)
Combining the data - Result
60
50
40
Further
Education
30
HE Private
20
10
HE
Government
0
Region
Any Questions?