Improving Migration and Population Statistics Improvements to Population Statistics Richard Pereira...

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

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

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

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

Interest in people

Funding– Funding formula

• Population related

– Specific grants• Total population• Sub-populations

Service delivery Service planning Policy / strategy Monitoring

Interest in people

Monitoring change– Population level– Sub-groups

Monitoring effectiveness– Policy– Strategy– Performance

Same but different

Devolution Treasury vs Barnett 3 year settlements Divergence of policy

Issues

When do we need the information? Change and instability Is that everyone? BUT

– Estimates more reliable– Are finance distribution– systems too rigid?

Questions?

nick.holmes@dataunitwales.gov.uk029 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

GLADEMOGRAPHY

The 15-minute Rant

26 June 2009RSS

GLADEMOGRAPHY

‘It is a truth universally acknowledged …’

Jane Austin – Pride and Prejudice

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

GLADEMOGRAPHY

But that is not an easy task.

Migration, Migration, Migration.

Especially International moves

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

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

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%

GLADEMOGRAPHY

NMGi in London

GLADEMOGRAPHY

How can we tell if estimates are right?

‘Sense Check’ results

Trends in:General Fertility RateStandardised Mortality RateSex RatiosAge structureHouseholds

GLADEMOGRAPHY

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

England and Wales Greater London Camden

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

2001 2006

GLADEMOGRAPHY

Age Structure

2001 2007

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

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

GLADEMOGRAPHY

My Prejudices

ONS to:

Maintain and publish New Migrant Databank

Develop LA Demographic Dashboard

GLADEMOGRAPHY

John.Hollis@london.gov.uk020 7983 4604

Adjusting internal migration estimates using data on students

Cal Ghee, Nicky Rogers, Jonathan Smith

Migration Statistics Improvement, ONSCD

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

500,000

1,000,000

1,500,000

2,000,000

2,500,000

2000/01 2007/08

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

18 19 20 21 22 23 24 25 26 27 28 29

single year of age 18-29

GP registrationstocks mid-2001

Census-based populationestimate mid-2001

Former students moving out of example area

18 19 20 21 22 23 24 25 26 27 28 29

HESAstudentnumbers

mid-year estimates

18 19 20 21 22 23 24 25 26 27 28 29

continued ageing onnot reflected inHESA student numbers

HESAstudentnumbers

mid-year estimates

18 19 20 21 22 23 24 25 26 27 28 29

HESAstudentnumbers

mid-year estimates

18 19 20 21 22 23 24 25 26 27 28 29

HESAstudentnumbers

mid-year estimates

20032004

18 19 20 21 22 23 24 25 26 27 28 29

HESAstudentnumbers

mid-year estimates

20032004 2005

18 19 20 21 22 23 24 25 26 27 28 29

HESAstudentnumbers

mid-year estimates

20032004 2005 2006

18 19 20 21 22 23 24 25 26 27 28 29

HESAstudentnumbers

mid-year estimates

20032004 2005 2006 2007

18 19 20 21 22 23 24 25 26 27 28 29

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

university

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

Assumptions:

• Reference date of move

• Overseas students

• Missing data

Reference date of move

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

university

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

university

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

≤ -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

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

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

Indicative results: Ceredigion with adjustment

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

Adjusted

Indicative results: Ceredigion with counter-adjustment

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

Adjusted with counter-adjustment starting after 3 years

Indicative results: Ceredigion mid 2007 population

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

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

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

• Government Office Region (GOR) & Wales level• IPS data calibrated to Labour Force Survey (LFS) data

• Local authority level • 2001 Census data

Current methods: out-migration

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

• 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

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

Birmingham

Westminster

Tow er Hamlets

Hackney

Herefordshire

Canterbury

Sheffield

Nottingham

Oxford

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

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

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

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)

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

• Regional level• IPS data calibrated to Labour Force Survey (LFS) data

• Local authority level • Model based distribution using administrative sources

Impact of changes - Out-migration

• 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)

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

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…

100,000

200,000

300,000

400,000

500,000

600,000

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

≤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

Indicative Results: LA level

< 1% 1% - 2% 3% - 4% 5% - 9% > 10%

Short-term Migration as a proportion of mid-2007 population estimate

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

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

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

PMMMMM

Typical Rogers Curve

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Basingstoke – A real example

Internal out migration rates males

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+

2002-06 MYE Based

2002-06 Rogers

Basingstoke – A real example

Internal out migration rates males

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90+

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

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

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 Local.migration.indicators@ons.gov.uk

Keeping up to date

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

• Joint ONS/LGA workshops• Implementation seminars• Consultation• Email: imps@ons.gsi.gov.uk

Q&A Panel

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