1. Improving Migration and Population Statistics Improvements to Population Statistics Richard Pereira Head of Migration Research Centre for Demography

2. Domestics Fire Exits Fire Alarm Refreshments Lunch at about 12:30 Tea and Coffee about 2:30 Close at about 4:00 Toilets ONS Facilitators Delegate Packs Questions

3. 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

4. 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

5. 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

6. Agenda 11:00 Morning session Introduction Package of Improvements and Timetable Views from LGA Adjusting internal migration using data on students 12:30 Lunch 1:00 Afternoon session International migration – modelling the geographical distribution of long-term migrants Short-term migrants at local authority level 2:30 Tea Break Other improvements Question panel 4:00 Close

7. Improving Migration and Population Statistics Background and Context Jonathan Swan Head of Change Management, ONSCD Centre for Demography

8. 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

9. Context – Population change

10. 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.

11. 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

12. 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

13. 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

14. 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;

15. The Population Statistics Improvement 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

16. The Population Statistics Improvement Strategy Medium 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

17. The Package Improvements in the 2008 round 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

18. 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

19. 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 government Information from ONS, DWP, and the Home Office A more coherent message interpreting the statistics

20. 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

21. 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

22. 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

23. 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

24. 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

25. Nick Holmes, Head of Data Development and Support Local Government Perspective ONS seminars on Improvements to Population Statistics

26. Local government perspective ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009 Interest in people Same but different Issues

27. 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

28. 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

29. 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

30. 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?

31. Local government perspective ONS seminars on Improvements to Population Statistics – Cardiff 19 June 2009 Questions? nick.holmes@dataunitwales.gov.uk 029 2090 9500 Questions guaranteed, answers are not

32. Improving population statistics – a local government perspective Jill Mortimer Local Government Association Improving Population Statistics - a local government perspective

33. 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

34. 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

35. 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

36. 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

37. Who is still missing? Those missed at 2001 Census Missed long-term migrants Misallocated internal migrants

38. Longer-term improvements 2009 improvements to IPS sample 2011 census E-borders New health sector recording system Better student data

39. Knotty problems Fluctuations in local estimates Two 2008 denominators for performance indicators Imperfections in administrative systems Students leaving college

40. DMA G GLADEMOGRAPHY The 15-minute Rant 26 June 2009 RSS

41. DMA G GLADEMOGRAPHY ‘It is a truth universally acknowledged …’ Jane Austin – Pride and Prejudice

42. DMA G 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

43. DMA G GLADEMOGRAPHY But that is not an easy task. Migration, Migration, Migration. Especially International moves

44. DMA G 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

45. DMA G 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

46. DMA G GLADEMOGRAPHY New Migrant Databank Leeds 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%

47. DMA G GLADEMOGRAPHY NMGi in London

48. DMA G GLADEMOGRAPHY How can we tell if estimates are right? ‘Sense Check’ results Trends in: General Fertility Rate Standardised Mortality Rate Sex Ratios Age structure Households

49. DMA G GLADEMOGRAPHY GFR

50. DMA G GLADEMOGRAPHY Sex Ratios

51. DMA G GLADEMOGRAPHY Age Structure

52. DMA G GLADEMOGRAPHY Households: Westminster CLG 2006-based Household Projections 100,200 113,000 Change 12,800 Actual New Homes 2001-06 4,700 MYE may be 10% too high

53. DMA G GLADEMOGRAPHY UKSA Interim Report on Migration Statistics - Recommendations ONS 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

54. DMA G GLADEMOGRAPHY My Prejudices ONS to: Maintain and publish New Migrant Databank Develop LA Demographic Dashboard

55. DMA G GLADEMOGRAPHY John.Hollis@london.gov.uk 020 7983 4604

56. Adjusting internal migration estimates using data on students Cal Ghee, Nicky Rogers, Jonathan Smith Migration Statistics Improvement, ONSCD Centre for Demography

57. Summary Context Current population estimates method Issues with estimating internal migration Solution using administrative data on students Indicative results

58. Context: Higher education (HE) student numbers in the UK

59. Context: HE student numbers 2007/08 2.3 million HE students 0.65 million 1 st year undergraduates within England & Wales Represents 1% of the total England & Wales population

60. Current population estimates method Estimated 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

61. 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

62. 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

63. An example of students moving to a ‘university LA’ to study

64. Former students moving out of example area

72. 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

73. Solution using HESA data

74. 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

75. HESA data Postcode and date of birth detail disclosive Preparing to lay regulation to gain access Future development

76. HESA data quality assessment Check that HESA data will meet our needs for adjustment Process: data evaluation Check for incomplete or duplicate records Sense check ages and dates Record frequency counts for key variables Production of datasets to be used in adjustment

77. 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%3 Source: HESA data

78. Proposed method

79. 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

80. Moves to study: LA to LA student adjustment approach

81. Assumptions i.Missing data ii.Term-time residence remains same up to June 30 th

82. 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

83. 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

84. a) Number students leaving university Data direct from HESA: Number people who end studies each year By term-time LA

85. a) Number students leaving university Assumptions: Reference date of move Overseas students Missing data

86. Reference date of move

87. a) Number students leaving university Assumptions: Reference date of move Overseas students Missing data

88. 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

89. 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

90. 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

91. 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

92. d) Allocation to first destination after studies Data: 2001 Census Method: Based on distribution of 2001 Census identifiable HE qualifiers’ moves

93. 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

94. 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

95. 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

96. Back series: Assumptions Students’ campus to residence patterns have remained constant for the period 2001 to 2008 Major expansions and mergers of campuses

97. 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

98. 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

99. Indicative results

100. 2007/08 indicative results for England and Wales

101. Indicative results: Size of adjustment for England & Wales LAs

102. Indicative results: Ten largest increases

103. Indicative results: Ten largest decreases

104. Indicative results: Adjustment for former students to first destinations

105. Indicative results: Ceredigion original estimates

106. Indicative results: Ceredigion with adjustment

107. Indicative results: Ceredigion with counter-adjustment

108. Indicative results: Ceredigion mid 2007 population

109. Summary New detail in HESA data available from 2009 Students moving to study Former students’ first destinations Back series Counter adjustment Indicative results

110. Questions?

111. International Migration: Modelling the Geographical Distribution of Long-term Migrants Jon Smith Migration Statistics Improvement Work Programme Ruth Fulton, Jane Naylor Demographics Methods Centre and Centre for Demography

112. The importance of international migration Key driver of population change

113. 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

114. 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

115. England & Wales

116. GOR & Wales

117. Intermediate Geography

118. Local Authorities

119. 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

120. 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)

121. 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)

122. 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

123. 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

124. In-migration Modelling at Local Authority (LA) Level

125. 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

126. 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

127. 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

128. Comparison of Flag 4s and NINos

129. 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

130. Model description IPS direct estimate used as the response variable –Uses required definition of long-term migrant Poisson model used –Appropriate for count data

131. Poisson distribution JanMayMarNovSepJulFebJunAprDecOctAug

132. 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

133. Choice of covariates Covariates selected which are associated with in-migration Direct - counts of actual migrants Indirect - factors associated with migration

134. Variables entered for potential selection NINoS Country of Birth Ethnic Population Flag 4s UK-born In-migrants Population Density Foreign Armed Forces Industry Mid-year Pop Est Foreign Students Job Centre Vacancies Home Armed Forces Internal Migration Unemp Estimates

135. Choice of covariates Model fitted for each year to identify most important covariates Fixed set of covariates then selected for use in all models

136. Fixed covariates currently in the model NINoS Country of Birth Ethnic Population Flag 4s UK-born In-migrants Population Density Foreign Armed Forces Industry Mid-year Pop Est Foreign Students Job Centre Vacancies Home Armed Forces Internal Migration Unemp Estimates

137. 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

138. 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

139. Out-migration Modelling at Local Authority Level

140. Aims achieved in this work Improve the robustness of the modelling approach Ensure consistency between the out- migration and in-migration models where appropriate

141. 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

142. 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

143. 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

144. 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)

145. 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

146. Fixed covariates currently in the model StudentsBangladeshi Ethnic group Shared Accommodation Population Density North American Country of birth Population aged 60 to 74 Overcrowding White Irish Ethnic group International in-migration Population Estimate

147. Impact of Changes

148. 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

149. 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

150. 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

151. Preliminary Impacts Assessment year to mid-2006

153. 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

154. Questions?

155. Short-term Migration Fiona Aitchison and Jonathan Smith IMPS Migration Research, ONSCD Centre for Demography

156. Summary Aims and Background Feasibility report Modelling method Indicative results Next steps

157. 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

158. Illustrated by…

159. 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)

160. 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

161. 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

162. 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

163. 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

164. Feasibility Report: Proposed Approach

165. 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

166. 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

167. 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)

168. Indicative Results: National / Regional

169. Indicative Results: LA level

171. Validation Statistical assessment of model diagnostics Comparison to administrative data sources Invite feedback from users

172. 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

173. Understanding and Measuring Uncertainty Associated with the Mid-Year Population Estimates Joanne Clements, Ruth Fulton, Jane Naylor Demographics Methods Centre and Centre for Demography

174. 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)

175. 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 Passenger Survey GP re-registrations (Internal migration)

176. Challenge Source data subject to sampling and non- sampling errors Survey Data Census Data Registration Data Administrative Data

177. Challenge How do we estimate each potential error and then combine these in one measure?

178. Project outline Aim Improve 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

179. 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

180. 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

181. Key Internal Migration Quality Issues Source LA for out-flows to 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

182. Key Internal Migration Quality Issues Time lags between moving and reregistering Moves not captured by GP registers because patients were not registered at one annual download Constraining GP register data to NHSCR Variation in 2001 base population in the Census and patient registers Potential double counting of school boarders Out-flows to Scotland / Northern Ireland and allocating these to LAs

183. 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

184. Improvements to Subnational Population Projections Modelling Internal Migration: Propensity to Migrate Jonathan Swan Head of Change Management, ONSCD Centre for Demography

185. 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

186. 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

187. 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

188. 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

189. 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

190. Typical Rogers Curve

191. Basingstoke – A real example

193. 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

194. Port Survey Review: Improvements to estimating international migration from the International Passenger Survey (IPS) Suzie Dunsmith, Nigel Swier, Sarah Crofts, Briony Eckstein Migration Statistics Improvement, ONSCD Centre for Demography

195. 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

196. 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)

197. 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

198. 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

199. Next steps Evaluate impacts of improvements to weighting methodology Evaluate impacts of improvements to sample design Review impact on methodology for distributing below GOR

200. Migration Indicators Suzie Dunsmith, Nigel Swier, Sarah Crofts, Briony Eckstein Migration Statistics Improvement, ONSCD Centre for Demography

201. 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

202. Figure 1.1: IPS long-term international migration estimates, UK, 2000 – 2008 Source: International Passenger Survey (IPS) estimates of long-term international migration Notes: 1.Data for YE Mar 08, YE Jun 08 and YE Sep 08 are provisional 2.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

203. 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

204. 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

205. Local area indicators - functionality

206. 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 Report www.statistics.gov.uk/statbase/Product.asp?vlnk=15230 User feedback requested Local.migration.indicators@ons.gov.uk

207. Keeping up to date Quarterly updates and other information at www.statistics.gov.uk/imps www.statistics.gov.uk/imps Joint ONS/LGA workshops Implementation seminars Consultation Email: imps@ons.gsi.gov.uk

208. Q&A Panel