Improving Migration and Population Statistics
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Transcript of Improving Migration and Population Statistics
Improving Migration and Population Statistics
Improvements to Population Statistics
Richard Pereira
Head of Migration Research
Centre for Demography
Domestics
• Fire Exits• Fire Alarm• Refreshments
Lunch at about 12:30Tea and Coffee about 2:30Close at about 4:00
• Toilets• ONS Facilitators• Delegate Packs• Questions
Why we are here
• Importance of migration: – Key component of population change– Changing society– Economic situation
• Drivers for improvement work: – relevant statistics– multiple purposes and customers– timeliness, quality
Programme Vision
Migration and Population Statistics meeting user needs:- At the right time- Covering the relevant populations- Measuring change accurately (national and local)- Detecting turning points
And are trusted as authoritative:- Based on range of developed best up to date sources- Enhanced, transparent, sustainable, statistical methods- With quality measures
By highly engaged users
Aims of the day
• To gain an understanding of the proposed package of improvements for the 2008 round
• To see how these fit into the longer term strategy• A chance to influence the improvements
– Spot any issues with the improvements– Identify if we have missed anything– Identify where further supporting material may be
needed– Provide expert local insight
Agenda
11:00 Morning session• Introduction• Package of Improvements and Timetable• Views from LGA • Adjusting internal migration using data on students12:30 Lunch1:00 Afternoon session• International migration – modelling the geographical distribution
of long-term migrants• Short-term migrants at local authority level2:30 Tea Break• Other improvements• Question panel4:00 Close
Improving Migration and Population Statistics
Background and Context
Jonathan Swan
Head of Change Management, ONSCD
Centre for Demography
Context
• New and emerging sources – These provide a valuable opportunity to increase
the quality of population statistics– Here today to inform you about how we will intend
to use these sources– And to get feedback on these proposals
• And we are using this opportunity because– Migration is important - a key part of population
change– It is difficult to measure– So important we capitalise on admin sources
Context – 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
Communities & Local Government’s interest in Population & Migration Statistics
CLG and Migration:
• Migration issues for CLG - impacts on local areas and communities; incl. development of evidence and improving statistics
• Managing the impacts of migration - support for local service providers in managing change.
Communities & Local Government’s interest in Population & Migration Statistics
Use of population and migration statistics:
• Research and analysis of migration trends, patterns and impacts - use of migration estimates and local indicators
• Local government finance settlement - formula grant distribution use of population data
• Household projections - demographics main driver of household growth
Use of Population Data in Formula Grant Distribution System
• Data used in formula grant distribution has to be the best data
available on a consistent basis for all local authorities and available at
the time.
• ONS sub-national population projections• Used because resident population is key client group for most services
• Used projections for 2008, 2009 & 2010 from the revised 2004-based projections for current 3-year settlement (2008-09 to 2010-11)
• Next multi-year settlement will be calculated in 2010• Expect to use 2008-based projections as the latest data available
• Mid-year population estimates• Mainly used to express indicators as proportions of the population• Used mid-2006 estimates in current 3 year settlement• Expect to use mid-2009 estimates in next multi-year settlement in 2010
Welsh Assembly Government interest in Population & Migration Statistics
• Demographic change has implications for the planning and provision of wide range of public services in Wales eg education, health, planning
• Use of population and migration statistics:• Analysis of demographic and population trends for
Wales• Key data Set in Revenue Settlement Grant• Used for population and household projections
Use of Population Data in the Revenue Settlement Grant (Wales)
• Data used in the Revenue Settlement Grant has to be the best data available on
a consistent basis for all local authorities and available at the time.
• Mid-year population estimates• Mainly used as indicators within the Settlement for key age groups;
• Used to calculate the indicator weights (within regression model);
• Used mid-2008 estimates in the latest settlement (2009-10)
• Expect to use mid-2009 estimates in next settlement (2010-11)
• Sub-national population projections
• Good for WAG medium term planning. Used internally for indicative settlements, but not for allocations.
• Proposed for use in the Settlement (for multi-year settlements), however further discussion and analysis of accuracy and suitability of projections required before any decisions are taken.
• Multi-year settlements using these are the long-term plan for the WAG;
The Population StatisticsImprovement Strategy
Short Term• Use aggregate administrative data to improve
data on geographical distribution of migration• Provide additional sources of information on
migration• Provide information in a more accessible way• Obtaining data through legal gateways
The Population StatisticsImprovement StrategyMedium Term• More extensive use of admin sources
− Using record linking techniques, to supplement current sources of migration data
• Quantitative measures of quality• Improved timeliness• 2011 Census
Long Term• ‘Beyond 2011’ strategy• Address lists?• E-Borders
The PackageImprovements in the 2008 round
New MethodsNew Methods
Improving Existing ProductsImproving Existing ProductsNew productsNew products
•LA Level
Short-term migration•Quality measures
•Migration Indicators
• Earlier migration outputs•Improvements to Port
Survey •Other refinements to
methods
•Distribution of international migration using administrative data•Student adjustments using HESA
data
Improvements that change population estimates or projections.
• Distribution of international migration using administrative data
• Student adjustments using HESA data• Port survey improvements• Other refinements to existing methods
Reporting
• Migration web page• Annual Migration Report
Comprehensive overview of UK migration during 2008.
• Migration Statistics Quarterly Reports• Regular updates on research progress
• Coherent across governmentInformation from ONS, DWP, and the Home Office
A more coherent message interpreting the statistics
Communications and Engagement Strategy
• A formal quality assurance strategy• Interactive engagement at an early stage
– Reference Panels– ONS/LGA Workshops– Early round of Seminars– Regular Updates on the web
• Chance to comment on results– Local Insight Reference Panels– Formal Academic Peer Review– Formal Consultation– Additional round of Seminars with indicative impacts
A collaborative approach to involvement, to help improve the quality of the statistics
The timetable
• First Rollout of Migration Indicators 20 May 2009• Reference Panels - ongoing• LGA Workshops - ongoing• Seminars June 2009• Mid-2008 mid-year population estimates for LAs – 27 August 2009
– Short-term migration estimates at LA level - 27 August 2009• National Population Projections – 21 October 2009• Consultation Dec 2009 to Feb 2010
– Consultation on improvements in parallel with English SNPP assumptions– Additional seminars during consultation– Indicative impacts published at start of consultation
• Publish subnational projections for England (ONS) and Wales (WAG) – 27 May 2010
• Publish revised 2002 to 2008 estimates – 27 May 2010• Mid-2009 population estimates - August 2010
The Consultation
• Part of engagement and quality assurance• December 2009 to February 2010 (approx)• Consulting in parallel on:
– Assumptions for Subnational Population Projections– The improvements to population and migration statistics
• Will be supported by a major package of documentation:– Indicative numerical impacts of the improvements at LA level– Detailed methodological documentation– Reports from the reference panels and academic peer reviews
The Consultation
• Looking for comments that will help us improve the package
• Comments likely to:– Lead to refinements of methodology and its
implementation– Help shape future research
December Seminar Roadshows
• Supporting the formal consultation• Will be focussed on the numerical results• Will provide an opportunity to discuss and
feedback on the impacts• Dates and venues to be arranged
But will be in at least 4 locations across England and Wales
Most likely around 30 November to 11 December
Nick Holmes, Head of Data Development and Support
Local Government Perspective
ONS seminars on Improvements toPopulation Statistics
Local government perspective
ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009
Interest in people Same but different Issues
Local government perspective
ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009
Interest in people
Funding– Funding formula
• Population related
– Specific grants• Total population• Sub-populations
Service delivery Service planning Policy / strategy Monitoring
Local government perspective
ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009
Interest in people
Monitoring change– Population level– Sub-groups
Monitoring effectiveness– Policy– Strategy– Performance
Local government perspective
ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009
Same but different
Devolution Treasury vs Barnett 3 year settlements Divergence of policy
Local government perspective
ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009
Issues
When do we need the information? Change and instability Is that everyone? BUT
– Estimates more reliable– Are finance distribution– systems too rigid?
Local government perspective
ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009
Questions?
[email protected] 2090 9500
Questions guaranteed,answers are not
Improving population statistics – a local government perspective
Jill Mortimer Local Government Association
Improving Population Statistics - a localgovernment perspective
Outline
• Why do the figures matter to local government?
• What are the problems with the figures?• What should have improved for the next
funding round?• Who is still missing?• Longer-term improvements• Knotty problems
Why do the figures matter?
• Accurate data underpins good service planning and delivery – for customer insight
• Accurate denominators for performance indicators – for resource allocation
• Accurate information for citizens – ‘evidence’ to trump ‘anecdote’
• Accurate information for funding settlement – to afford key workers
What are the problems?
• Long-term migrants missed in IPS• Undercount from 2001 census• Short-term migrants uncounted in
population estimates• Inaccurate distribution around the
country• Internal migration inaccuracies• Different sources paint different pictures
What should improve by 2010?
• Internal migration estimates (of students)
• Distribution across country (provided this includes regional distribution)
• Short-term migration figures for local areas
Who is still missing?
• Those missed at 2001 Census• Missed long-term migrants• Misallocated internal migrants
Longer-term improvements
• 2009 improvements to IPS sample• 2011 census• E-borders• New health sector recording system• Better student data
Knotty problems
• Fluctuations in local estimates• Two 2008 denominators for
performance indicators• Imperfections in administrative
systems• Students leaving college
DMAG
GLADEMOGRAPHY
The 15-minute Rant
26 June 2009RSS
DMAG
GLADEMOGRAPHY
‘It is a truth universally acknowledged …’
Jane Austin – Pride and Prejudice
DMAG
GLADEMOGRAPHY
‘… that a country that is one of the world’s top economies should be able to accurately estimate the population of administrative areas and do so in a timely manner.’
John Hollis – personal prejudice
DMAG
GLADEMOGRAPHY
But that is not an easy task.
Migration, Migration, Migration.
Especially International moves
DMAG
GLADEMOGRAPHY
Why we need good local migration estimates
LA and HA settlements => based on population projections => based on population estimates => based on migration estimates
Good LA estimates => better small area estimates
Estimates underline indicators (IMD, etc)
QA for 2011 Census
DMAG
GLADEMOGRAPHY
Where do we need to start?
Regional Distribution
University of Leeds New Migrant Databank
LA Distribution
Review/do away with NMGi in London – and maybe elsewhere
DMAG
GLADEMOGRAPHY
New Migrant DatabankLeeds University ESRC UPTAP project
Already used HESA/NINo/Flag 4 to break TIM down to regions:
London +20k +12%West Midlands +11k +33%North West +4k +8%
East -14k -23%Yorks and Humber -10k -21%South West -8k -19%South East -4k -4%
DMAG
GLADEMOGRAPHY
NMGi in London
DMAG
GLADEMOGRAPHY
How can we tell if estimates are right?
‘Sense Check’ results
Trends in:General Fertility RateStandardised Mortality RateSex RatiosAge structureHouseholds
DMAG
GLADEMOGRAPHY
GFR
45.0
50.0
55.0
60.0
65.0
70.0
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06
Gen
eral
Fer
tili
ty R
ate
England and Wales Greater London Camden
DMAG
GLADEMOGRAPHY
Sex Ratios
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
1.1000
1.2000
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 70-74 75-79 80-84
Sex
Rat
io
2001 2006
DMAG
GLADEMOGRAPHY
Age Structure
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
2001 2007
DMAG
GLADEMOGRAPHY
Households: Westminster
CLG 2006-based Household Projections 100,200 113,000Change 12,800
Actual New Homes2001-06 4,700
MYE may be 10% too high
DMAG
GLADEMOGRAPHY
UKSA Interim Report on Migration Statistics - RecommendationsONS to make clear to what extent revisions are an improvement
ONS to engage users fully re methodology – use LAs to help QA
ONS to flag reliability of LA estimates
Better communication of work being done
The LGA (and others) work with ONS to get wider LA engagement
DMAG
GLADEMOGRAPHY
My Prejudices
ONS to:
Maintain and publish New Migrant Databank
Develop LA Demographic Dashboard
Adjusting internal migration estimates using data on students
Cal Ghee, Nicky Rogers, Jonathan Smith
Migration Statistics Improvement, ONSCD
Centre for Demography
Summary
• Context
• Current population estimates method
• Issues with estimating internal migration
• Solution using administrative data on
students
• Indicative results
Context: Higher education (HE) student numbers in the UK
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
2000/01 2007/08
nu
mb
er o
f st
ud
ents
overseas origin
UK origin
Context: HE student numbers 2007/08
• 2.3 million HE students
• 0.65 million 1st year undergraduates within England & Wales
• Represents 1% of the total England & Wales population
Current population estimates methodEstimated resident population at time T
Natural Change – add births, subtract deaths
International migration – add inflows, subtract outflows
Internal migration – add inflows, subtract outflows
Add special populations back in
Estimated resident population at time T+1
Remove special populations – UK armed forces, foreign armed forces, prisoners, school boarders
Age-on population by 1 year
Issues to be addressed by the new methodology…
• Some young people, particularly young men, not changing their GP registration soon after they move
• Students a sub-set of young people, who necessarily cluster in certain areas of the country
• Affects estimation of students moving to university and moving away after their studies
• Some encouragement to change GP registration at start of studies, but no encouragement when students leave
Recommendations from earlier work
• “To investigate further the feasibility of making a student adjustment or treating students as a special population group”
Source: 2007 Welwyn Hatfield LA case study report
• “Additional information on student migrants should be collected by the Higher Education Statistics Agency (HESA) and access to individual level data provided for linking with other sources”
Source: Report of the Inter-departmental Task Force on Migration Statistics 2006
An example of students moving to a ‘university LA’ to study
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
single year of age 18-29
po
pu
lati
on
GP registrationstocks mid-2001
Census-based populationestimate mid-2001
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
HESAstudentnumbers
mid-year estimates
2001
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
continued ageing onnot reflected inHESA student numbers
HESAstudentnumbers
mid-year estimates
2001
2002
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
continued ageing onnot reflected inHESA student numbers
HESAstudentnumbers
mid-year estimates
2001
2002
2003
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
continued ageing onnot reflected inHESA student numbers
HESAstudentnumbers
mid-year estimates
2001
2002
20032004
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
continued ageing onnot reflected inHESA student numbers
HESAstudentnumbers
mid-year estimates
2001
2002
20032004 2005
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
continued ageing onnot reflected inHESA student numbers
HESAstudentnumbers
mid-year estimates
2001
2002
20032004 2005 2006
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
continued ageing onnot reflected inHESA student numbers
HESAstudentnumbers
mid-year estimates
2001
2002
20032004 2005 2006 2007
Former students moving out of example area
0
500
1,000
1,500
2,000
2,500
18 19 20 21 22 23 24 25 26 27 28 29
2001
2007
Proposed student migration adjustments
• Proposed adjustment to estimates of migration within England & Wales using HESA data
• Linking HESA and International Passenger Survey (IPS) data to identify overseas students is more complex
• Use of HESA data to improve international migration is planned
Solution using HESA data
Solution: what’s new?
• Higher Education Statistics Agency (HESA) data
• Data on all HE students
• New term-time postcode detail collected by HESA for all institutions from 2007/08 academic year
• New detail received March 2009
HESA data
• Postcode and date of birth detail disclosive
• Preparing to lay regulation to gain access
• Future development
HESA data quality assessment
• Check that HESA data will meet our needs for adjustment
• Process: data evaluationCheck for incomplete or duplicate records
Sense check ages and dates
Record frequency counts for key variables
Production of datasets to be used in adjustment
2007/08 HESA data quality
• Domicile (origin): data for 98% of student population
• Term-time address: data for 87% of student population
% records missing term-time postcode
Number campuses
0-9% 156
11-24% 26
25-49% 10
50-74% 9
75-99% 2
100% 3Source: HESA data
Proposed method
Adjusting mid-2008 & back series
• Estimates of students going to university
• Estimates of former students leaving
university
• Creating a back series for the above for
estimates for 2002 – 2007
• Creating a counter-adjustment
Moves to study:LA to LA student adjustment approach
Assumptions
i. Missing data
ii. Term-time residence remains same up
to June 30th
Adjusting mid-2008 & back series
• Estimates of students going to university
• Estimates of former students leaving
university
• Creating a back series for the above for
estimates for 2002 – 2007
• Creating a counter-adjustment
Estimates of former students leaving university
How many people:
a) Leave university
b) Move to a different LA
c) And don’t change registration with a GP
d) Remove former students from the LAs they were previously resident in and allocate them to the LAs they move to
a) Number students leaving university
Data direct from HESA:
• Number people who end studies each year
• By term-time LA
a) Number students leaving university
Assumptions:
• Reference date of move
• Overseas students
• Missing data
Reference date of move
a) Number students leaving university
Assumptions:
• Reference date of move
• Overseas students
• Missing data
b) Former students leaving LA
Data: Based on 2001 Census
Method: Calculate rate at which graduates
left LA based on 2001 Census data for
identifiable HE qualifiers’ moves 2000-2001
b) Former students leaving LA
Assumptions:
• Rates remained constant since 2001
• Graduates on 3 year undergraduate degrees and 1 year postgraduate degrees
• Rate applicable up to age 28
c) Leave LA but don’t change GP registration
Data: GP registers & 2001 Census
Method: Based on rates from 2000/2001 GP
registrations and Census migration data
c) Leave LA but don’t change GP registration
Assumptions:
• Rate for all young people is valid for students at the end of their studies
• Rates have remained constant since 2001
d) Allocation to first destination after studies
Data: 2001 Census
Method: Based on distribution of 2001 Census
identifiable HE qualifiers’ moves
d) Allocation to first destination after studies
Assumptions:
• Destinations have remained constant since 2001
• Rates apply to all ages up to 28
• Students who withdraw from studies have the same destinations as qualifiers
Adjusting mid-2008 & back series
• Estimates of students going to university
• Estimates of former students leaving
university
• Creating a back series for the above for
estimates for 2002 – 2007
• Creating a counter-adjustment
Back series: Method
• Apply term-time residence patterns of 2007/08 students back to 2002
• Students to study using same method as for 2007/08
• Former students adjustment using same method as for 2007/08
Back series: Assumptions
• Students’ campus to residence patterns have remained constant for the period 2001 to 2008
• Major expansions and mergers of campuses
Adjusting mid-2008 & back series
• Estimates of students going to university
• Estimates of former students leaving
university
• Creating a back series for the above for
estimates for 2002 – 2007
• Creating a counter-adjustment
Counter-adjustments for double counting
• Problem isn’t that young people never change their GP registration – just that they are slow to do so
• Danger of double-counting moves when someone does eventually change GP registration
• Implemented counter-adjustment for adjusted moves gradually over adjustment period
Indicative results
2007/08 indicative results for England and Wales
10,93824,37935,317Proposed ‘end of study’adjustment
30,30036,00066,300Proposed ‘start of start of study’adjustment
FemalesMalesTotal
10,93824,37935,317Proposed ‘end of study’adjustment
30,30036,00066,300Proposed ‘start of start of study’adjustment
FemalesMalesTotal
Indicative results: Size of adjustment for England & Wales LAs
0
50
100
150
200
250
300
≤ -4,000 `-2,000 to -3,999 `-1,000 to -1,999 `-1 to -999 0 to 999 1,000 to 1,999 2,000 to 3,999 ≥ 4,000
Size of Adjustment
Nu
mb
er
of L
As
Indicative results: Ten largest increases
Local Authority Total AdjustmentManchester 8000Lambeth 6000Wandsworth 5500Southwark 5500Salford 5300Birmingham 5100Tower Hamlets 5100Westminster 4900Kingston Upon Hull UA 4200South Gloucestershire 4100
Indicative results: Ten largest decreases
Local Authority Total AdjustmentOxford -6400Cambridge -5000Durham -3300Stockport -1900Lancaster -1800Macclesfield -1600Colchester -1600York UA -1500Harrogate -1400Powys UA -1300
Indicative results: Adjustment for former students to first destinations
Indicative results: Ceredigion original estimates
0
500
1,000
1,500
2,000
2,500
3,000
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
single year of age, 10-35
po
pu
lati
on
2001
2002
2003
2004
2005
2006
2007
Indicative results: Ceredigion with adjustment
0
500
1,000
1,500
2,000
2,500
3,000
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
single year of age, 10-35
po
pu
lati
on
2001
2002
2003
2004
2005
2006
2007
Adjusted
Indicative results: Ceredigion with counter-adjustment
0
500
1,000
1,500
2,000
2,500
3,000
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
single year of age, 10-35
po
pu
lati
on
2001
2002
2003
2004
2005
2006
2007
Adjusted with counter-adjustment starting after 3 years
Indicative results: Ceredigion mid 2007 population
0
500
1,000
1,500
2,000
2,500
3,000
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
single year of age, 10-35
po
pu
lati
on
original
adjusted
adj+cadj3yrlag
2001 pattern
mid-2007
Summary
• New detail in HESA data available from 2009• Students moving to study• Former students’ first destinations• Back series• Counter adjustment• Indicative results
Questions?
International Migration:Modelling the Geographical Distribution of Long-term Migrants
Jon SmithMigration Statistics Improvement Work Programme Ruth Fulton, Jane Naylor
Demographics Methods Centre
Demographics Methods Centreand Centre for Demography
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
The challenge of producing estimates
• No system of compulsory migration registration
• Rapid changes in levels and distribution
• Increasingly complex patterns
• Estimates required at local authority, region and national levels
Current methods: in-migration
• 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
England & Wales
GOR & Wales
Intermediate Geography
Local Authorities
Current methods: in-migration
• 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 methods: out-migration
• 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)
Previous improvements (2007)
• Regional level (in-migration)• Calibration of IPS to LFS at regional level –
changing intended to actual destination
• Intermediate geography level• Introduction of a bespoke intermediate geography
for both in-migration and out-migration (NMGi, NMGo)
Previous improvements (2007)
• Local authority level (out-migration)• Model based distribution (propensity to migrate)
• Improvements to sub-national age distributions• In and out-migration
• Changes to assumptions on those who change their intended length of stay
Planned improvements (2009)
Local authority level
• In-migration• Replacing the Census distribution with a model
based approach using administrative data sources
• Out-migration• Improving the model introduced in 2007
In-migration Modelling at Local Authority (LA) Level
Modelling in-migration
• Current method uses 2001 Census data to distribute to LA level
• Clear changes in migration trends since 2001
e.g. EU accession
• Concept proved with introduction of local authority out-migration models in 2007
What modelling achieves
• Improves timeliness at LA level
• Potential use of administrative data
• GP registrations (Flag 4s)
• National Insurance Number (NINo) allocations to overseas nationals
• Annually updated counts available
• Provide counts at local authority level
Use of administrative data
Modelling helps us deal with issues such as:
• Coverage
• Definition of a migrant
• Inconsistency over time
While administrative sources can’t be used directly, they can be used in a model
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
• Combines information from several administrative data sources, and can also include additional covariates, such as area characteristics.
• Modelling process identifies the relative importance of the variables entered
Model description
• IPS direct estimate used as the response variable
– Uses required definition of long-term migrant
• Poisson model used
– Appropriate for count data
Poisson distribution
Jan MayMar NovSepJulFeb JunApr DecOctAug
Model description
• Model fitted at LA level and coefficients estimated
• Predicted values for LAs calculated using these coefficients
• Use to distribute Intermediate geography estimate to LA level
Choice of covariates
• Covariates selected which are associated with in-migration
Direct - counts of actual migrants
Indirect - factors associated with migration
Variables entered for potential selection
NINoS
Country of Birth
EthnicPopulation
Flag 4sUK-born
In-migrantsPopulation
Density
Foreign Armed Forces
Industry
Mid-yearPop Est
ForeignStudents
Job CentreVacancies
Home Armed Forces
InternalMigration
UnempEstimates
Choice of covariates
• Model fitted for each year to identify most important covariates
• Fixed set of covariates then selected for use in all models
Fixed covariates currently in the model
NINoS
Country of Birth
EthnicPopulation
Flag 4sUK-born
In-migrantsPopulation
Density
Foreign Armed Forces
Industry
Mid-yearPop Est
ForeignStudents
Job CentreVacancies
Home Armed Forces
InternalMigration
UnempEstimates
Validation checks carried out
• Standard model diagnostics
• Comparing the 2001 model based estimates with the 2001 Census data
• Comparing the sum of the model based estimates for LAs within an NMGi with the NMGi estimate
• Checking the time-series
Methodology for London
Existing methodology (within London)
• Student population is distributed directly to LA level using a Census distribution
• Non-student population is distributed to NMGi level using LFS data, and a Census distribution below this.
New methodology
• Distribute student population to NMGi level using a Census distribution, and distribute non-student population to NMGi level using LFS data
• Then use model based estimates to distribute NMGi total to LA level
Out-migration Modelling at Local Authority Level
Aims achieved in this work
• Improve the robustness of the modelling approach
• Ensure consistency between the out-migration and in-migration models where appropriate
Improvements
• Fits model at local authority level rather than intermediate geography level
• Uses Poisson modelling
• Tested some additional covariates, e.g. more detailed ethnic group
Improvements
• Includes an Intermediate geography and/or GOR effect
• Models number of migrants rather than propensity to migrate
• Expresses covariates as counts rather than proportions
• Fixes the set of covariates
Differences from in-migration model
• Averages IPS data over 3 years
• Includes an Intermediate geography effect
• Includes covariates which are associated with out-migration
• Does not include any direct counts of out-migrants
Variables entered for potential selection
Annually updated:
• ONS mid-year population estimates (split into age/sex groups)
• Annual Population Survey (APS) economic activity data, working lone parents
• ONS unemployment estimates• Foreign & Home armed forces • International in-migration • Internal migration (in and out) • Population density • Life expectancy of females (ONS)• ONS ethnic population estimates • Property and person crime (Home Office)
Variables entered for potential selection
2001 Census variables:• Household reference person (HRP) under 25• Persons with limiting long term illness (split into
age groups)• Foreign students, All students • Country of birth• Socio-economic classification of HRP• Higher educational qualifications• Central heating• Sole use of bath/ toilet• Tenure • Overcrowding, Household size
Fixed covariates currently in the model
Students Bangladeshi Ethnic group
Shared Accommodation
Population Density
North AmericanCountry of birth
Population aged60 to 74 Overcrowding
White Irish Ethnic group
International in-migration
Population Estimate
Impact of Changes
Impact of changes - In-migration
• National level• International Passenger Survey (IPS) data only
• Regional 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 • Model based distribution using administrative sources
Impact of changes - Out-migration
• National level• International Passenger Survey (IPS) data only
• Regional level• IPS data only
• Intermediate geography level• IPS data only
• Local authority level • Refined model based distribution
Impact of model based distribution
• The NMGi and NMGo totals won’t change
• Only affects the distribution of number of in-migrants and out-migrants within the intermediate geography
• Migration estimates for local authorities will change for mid-2002 to mid-2008 as a result
Preliminary Impacts Assessment year to mid-2006
1118-500 to -999
48<= -1,000
106110-100 to -499
172147-99 to 99
6063100 to 499
1117500 to 999
1213>= + 1,000
No. of LAs (out-migration)
No. of LAs (in-migration)
Average Annual Impact
1118-500 to -999
48<= -1,000
106110-100 to -499
172147-99 to 99
6063100 to 499
1117500 to 999
1213>= + 1,000
No. of LAs (out-migration)
No. of LAs (in-migration)
Average Annual Impact
Preliminary Impacts Assessment year to mid-2006
year to mid-06 (net change as % of mid-year estimate)
0
20
40
60
80
100
120
140
160
exce
ed
s -
1%
-0.8
% to
-1
%
-0.6
% to
-0
.8%
-0.6
% to
-0
.4%
-0.4
% to
-0
.2%
-0.2
% to
0%
0%
to
0.2
%
0.2
% to
0.4
%
0.4
% to
0.6
%
0.6
% to
0.8
%
0.8
% to
1%
exce
ed
s 1
%
Future model development
• Modelling approach further refined with other work being undertaken as part of the improvement programme:
• Port Survey Review • Access to administrative sources• Short-term migration estimates
Questions?
Short-term Migration
Fiona Aitchison and Jonathan SmithIMPS Migration Research, ONSCD
Centre for Demography
Summary
• Aims and Background
• Feasibility report
• Modelling method
• Indicative results
• Next steps
Aims for the local level short-term migration estimates
• To meet user demand to identify areas with high levels of short-term migration
• To help make comparisons between migration estimates and administrative sources
• To help explain growth in total migration numbers
Illustrated by…
0
100,000
200,000
300,000
400,000
500,000
600,000
Year
Mig
rant
s
Long-Term Migration Short-Term Migration NINo
2004 20052003
Background
• Short-term migration estimates are a new product, first produced in 2007 and are experimental statistics
• Available on a number of definitional bases to meet user requirements
• Reason for visit: employment, study or other reasons • Length of stay: 1 to 12 months or 3 to 12 months
• Estimates currently published at national level (England & Wales)
Background
• Feasibility Report on local area level estimates published in November 2008
• First estimates of short-term in-migration at local authority level are planned to be published in August 2009
Feasibility Report: Key Decisions
• Local authority level estimates for areas within England and Wales
• For the year to mid-2007
• For in-migration
• Estimates of the flow of short-term migrants
Feasibility Report: Key Decisions
• Definition to be used at local area level:
“Moves made for between 1 and 12 months for all reasons”
• Decision based on:• User responses to consultation• International Passenger Survey (IPS) sample sizes
• Key Implication:• The national level total to be distributed between local
authority areas for mid-2007 is 1,334,000
Feasibility Report: Proposed Approach
• IPS data are not robust enough to use directly at local authority level
• Proposed a model based approach similar to that for long-term in-migration:
• based on a Poisson distribution• based on weighted estimate of migrants • estimates using a range of administrative and other data
chosen to reflect short-term migration
Feasibility Report: Proposed Approach
Modelling: IPS Data
• Uses IPS completed flow data- More accurate as does not rely on intentions data
• Imputation techniques used to allocate records with no geographic information to LA areas
• Weighted estimates of short-term migrants entered as response variable
Modelling: Process
• Covariates associated with short-term migration entered into model
• Model then selects the covariates which are most important in explaining short-term migration
• Model run and estimates produced for mid-year 2007
• Directly at Local Authority level• At Unitary Authority/County level and then applied at
Local Authority level
Modelling: Potential Covariates
• NINo arrivals (split by nationality: A8 or non-A8)• WRS• Flag 4 (patient registers)• Students (2001 Census)• Students (HESA data)• ONS ethnic population estimates• Country of birth (2001 Census)• ONS unemployment estimates• Job Centre Plus vacancies• Long-term international in-migrants • Long-term international out-migrants• Businesses employing 250+ (from IDBR)• Businesses in Hotels and Catering industrial sector (from IDBR)• Seasonal Agricultural Workers Scheme (SAWS)
Indicative Results: National / Regional
1,334,000England & Wales
1,299,000England
35,000Wales
86,000South West
194,000South East
108,000East
478,000London
102,000West Midlands
86,000East Midlands
83,000Yorkshire and the Humber
130,000North West
32,000North East
Indicative EstimateArea
1,334,000England & Wales
1,299,000England
35,000Wales
86,000South West
194,000South East
108,000East
478,000London
102,000West Midlands
86,000East Midlands
83,000Yorkshire and the Humber
130,000North West
32,000North East
Indicative EstimateArea
Indicative Results: LA level
100
216
23 20
7 5 5
0
50
100
150
200
250
≤1,000 1,000 - 4,999 5,000 - 9,999 10,000 - 14,999 15,000 - 19,999 20,000 - 24,999 ≥ 25,000
Indicative size of LA level short-term in-migration estimate
Nu
mb
er
of
Lo
ca
l Au
tho
riti
es
Indicative Results: LA level
18
273
53
25
7
0
50
100
150
200
250
300
< 1% 1% - 2% 3% - 4% 5% - 9% > 10%
Short-term Migration as a proportion of mid-2007 population estimate
Nu
mb
er
of
Lo
ca
l Au
tho
riti
es
Validation
• Statistical assessment of model diagnostics
• Comparison to administrative data sources
• Invite feedback from users
Next Steps
• July 2009: Consult with Short-term Migration Reference Panel members pre-publication
• August 2009: Publish first LA level estimates for mid-2007 and invite feedback from users
• February 2010: Publish mid-2008 England & Wales estimates
• May 2010: Publish mid-2008 LA level estimates
Understanding and Measuring Uncertainty Associated with the Mid-Year Population Estimates
Joanne Clements, Ruth Fulton, Jane Naylor
Demographics Methods Centre
Demographics Methods Centreand Centre for Demography
Context
• Leading new international research
• Why are quality measures needed for
population estimates?
• Improving Migration and Population Statistics
(IMPS) Project – Quality strand
• ‘ONS should flag the level of reliability of
individual local authority population estimates’
(UK Statistics Authority)
Challenge
Estimates compiled from a wide range of administrative sources plus survey and Census data
Birth Registrations Asylum Seeker Applications
Death Registrations
Home Armed Forces Records
International PassengerSurvey
GP re-registrations(Internal migration)
Challenge
Source data subject to sampling and non-sampling errors
Survey Data
Census Data
Registration Data
Administrative Data
Challenge
How do we estimate each potential error and then combine these in one measure?
Project outline
• AimImprove understanding, measurement and reporting of the quality of population estimates
• Objectives– Describing the sources of uncertainty– Developing methods for measuring
uncertainty for each issue and combining them into one measure
– Feeding findings into published ONS quality reports
Methodology
• Map out the procedures and data sources used to derive population estimates
• Identify associated quality issues
• Attempt to quantify uncertainty using statistical theory & empirical evidence instead of expert opinion
• Combine individual measures of uncertainty by simulating potential errors in the data
• Provide information on other potential issues or sources of error
Progress
• Initial work proved feasibility of simulation methodology
• Focus now on sources of error with greatest impact; internal and international migration
• Currently focussing on internal migration
Key Internal Migration Quality Issues
Source LA for out-flowsto NI and Scotland
Census and 2001 Patient
Registers
Constraining GP register data to
NHSCR data
Time
Lags
Double counting of School boarders
Not registered
at mid-year
Reporting and Future Work
• Short update on progress – August 2009
• Detailed paper on internal migration findings
– November 2009
• Potential further work:
- international migration
- quantifying impact of methodological changes on
quality of estimates
Improvements to Subnational Population Projections
Modelling Internal Migration: Propensity to Migrate
Jonathan Swan
Head of Change Management, ONSCD
Centre for Demography
Wales Sub-national projections
• WAG responsibility
• Involvement of Wales Sub-national Population Projections Working Group
• Aim to publish in May 2010 using revised population base and revised migration data
• Same method as for 2006-based projections
Background
• We are building a new IT system to run the Subnational Population Projections (SNPPs)
• SNPPs in England use a ‘propensity to migrate method’
• We are improving the details within this methodology– These improvements address issues discovered
as a result of the last SNPP consultation
Two Changes
• Changing the method for averaging the migration rates over time
• Removing the Rogers Curve that is applied to age data– To use actual data
Summary of existing methodology
1. Average migration rates out of LA over latest five years (by SYOA and sex)
2. Smooth the age curves by calculating the Rogers curve
3. Calculate internal migration rates matrix (probability of moving from each LA to each other LA by SYOA and sex)
4. And then for each projection year apply these rates to the previous years (projected) population to give the number of migrants Sum to give LA total inflows
Calculating the average over time
• Existing Formula
• New Formula
New formula takes into account population levels over the full five years.
M = Migration
P = Population
y = Year
5
4321
PMMMMM
y
yyyyy
54
4
3
3
2
2
1
1
PM
PM
PM
PM
PM
y
y
y
y
y
y
y
y
y
y
Typical Rogers Curve
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Age
Lik
lieh
oo
d o
f m
igra
tin
g
Basingstoke – A real example
Internal out migration rates males
0.00
0.05
0.10
0.15
0.20
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+
Age
Lik
lieh
oo
d o
f m
igra
tin
g
2002-06 MYE Based
2002-06 Rogers
Basingstoke – A real example
Internal out migration rates males
0.00
0.05
0.10
0.15
0.20
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+
Age
Lik
lieh
oo
d o
f m
igra
tin
g
2001 MYE Based
Census Based
2002-06 MYE Based
2002-06 Rogers
Students
• Removal of the Rogers Curve means that the Student Adjustment to internal migration (based on HESA data) will feed through fully into the calculations for Subnational Population Projections
Port Survey Review:Improvements to estimating international migration from the International Passenger Survey (IPS)
Suzie Dunsmith, Nigel Swier, Sarah Crofts, Briony EcksteinMigration Statistics Improvement, ONSCD
Centre for Demography
International Passenger Survey (IPS)• Multi-purpose: (expenditure, tourism,
migration)• IPS samples passengers: (air, sea, tunnel)• UN “12 month” definition of an international
long-term migrant• Long-term migration data based on intentions
Port Survey Review (PSR)• To improve statistics on migrants entering and
leaving the country
Context of the Port Survey Review
Previous IPS improvements
2007• Migration ‘filter shifts’ for out-migration introduced
for the first time
2008• Improved coverage of some short-term migrants • Increased number of migrant contacts at ports
already included in the survey (in particular at Stansted, Luton and Manchester airports)
IPS improvements 2009
Operational• Introduction of additional ports (Belfast and
Aberdeen) • More efficient allocation of IPS shifts to better reflect
migrant flows at different ports• Fundamental sample design change
Processing• Improved methods for weighting and imputation• Improved IPS processing system
2007: Increased number of outflow contacts from under 800 to over 2300
2008: Incremental improvements expected
2009: Several major improvements expected- Migrant sample size potential increase of up to
50%- Overall standard errors for total inflows and
outflows to reduce from around 4% to under 3%- More balanced migrant sample
Impact of changes
Next steps
• Evaluate impacts of improvements to weighting methodology
• Evaluate impacts of improvements to sample design
• Review impact on methodology for distributing below GOR
Migration Indicators
Suzie Dunsmith, Nigel Swier, Sarah Crofts, Briony EcksteinMigration Statistics Improvement, ONSCD
Centre for Demography
Released in May 2009 – National level
• Provisional IPS estimates of long-term international migration– Rolling annual series updated quarterly– Tables showing estimates by citizenship and
reason for migration– Charts showing estimates over time
• Improved timeliness
Figure 1.1: IPS long-term international migration estimates, UK, 2000–2008
Source: International Passenger Survey (IPS) estimates of long-term international migrationNotes: 1.Data for YE Mar 08, YE Jun 08 and YE Sep 08 are provisional2.The relative standard errors for the latest immigration and emigration values are 4 per cent and 5 per cent respectively (please see Glossary for more information on standard errors)3.The IPS estimates of long-term international migration are not adjusted to account for asylum seekers, people migrating to and from the Republic of Ireland and people whose length of stay changes from their original intentions
Released in May 2009 - Local level
• Range of data sources at local level updated quarterly
• Initially based on already published data• Allows users to compare indicators for a
selected area• Allows users to compare areas for a selected
indicator
Local area indicators – content of first release
• Population turnover by LA• International migrant inflow by LA• Nationality (proportion of non-British
population)• Non-UK born (proportion of population not
born in the UK)• Migrant National Insurance Number (NINo)
registration
Local area indicators - functionality
Next steps
• Both national and local indicators will be updated quarterly where data sources allow
• New indicators will be added• Functionality will be improved• Indicators available via Migration Statistics Quarterly
Reportwww.statistics.gov.uk/statbase/Product.asp?vlnk=15230
• User feedback requested [email protected]
Keeping up to date
• Quarterly updates and other information at www.statistics.gov.uk/imps
• Joint ONS/LGA workshops• Implementation seminars• Consultation• Email: [email protected]
Q&A Panel