Monitoring UK internal migration in the twenty-first century John Stillwell Centre for Interaction Data Estimation and Research (CIDER), School of Geography, University of Leeds Presentation at Workshop on ‘Migration and Labour Markets: China and the UK’, University of St Andrews, June, 2010

Acknowledgements ESRC Census Programme grant to the Centre for Interaction Data Estimation and Research CIDER (RES ) Adam Dennett at CIDER for providing some of the slides on migration classification ONS for supplying data to CIDER

Introduction In recent times, press headlines have been dominated by immigration issues and the call for ‘balanced’ international migration

Importance of internal migration Destinations OriginsEnglandWalesScotlandN. IrelandTotal England5,153,43648,24843,6757,899 5,253,258 Wales42,614243,8511, ,336 Scotland42,8311,396473,7892, ,649 Northern Ireland8, , ,773 Total 5,247,693293,855521,612138,8566,202, Census records over 6.2 million migrants in the 12 months before the 2001 Census 467,000 immigrants arrived in the same period 406,800 individuals moved but we do not know where from because their origins were unstated

Questions What are the key sources of data on internal migration, especially between censuses? How has the magnitude, composition and pattern of internal migration changed since 2001 How do we monitor changing migration trends?

Sources of internal migration data Census of Population National Health Service patient re-registrations from the Central Register in England and Wales (and equivalents in Scotland and Northern Ireland) NHS patient registers Other administrative sources for different migrant subsets: e.g. HESA for students; School Census for school children; electoral roll for registered electors;.... Surveys such as LFS, APS,..

Census 2001 migrant data Comprehensive and reliable Migrants rather than migrations (i.e. transition data) but also wholly moving households and moving groups Available from ONS at different spatial scales: Districts (426), Wards (10,608), Output Areas (223,060) in the UK Special Migration Statistics are tables of origin- destination counts tables by age, sex, ethnicity, family status, economic activity, tenure and NS-SEC Adjustments for confidentiality are a particular problem at OA level

NHSCR movement data NHS patients re-registering with new doctors are captured by the NHS Central Registers Move or event data by age and sex Only flows between Health Authorities recorded Data for England and Wales obtained by ONS but not joined up with data for Scotland and for N. Ireland

Migration data from patient registers The Patient Register Data System (PRDS) is a compilation of all HA patient registers in England and Wales ONS compare records in one year with those of the previous year by linking on NHS number enabling identification of each person who changes their postcode Transition data (like census) Patient register data and NHSCR data combined to produce estimates of population movements for mid- year to mid-year periods for local authority districts in England and Wales by broad age group These data now available through the Web-based Interface to Census Interaction Data (WICID)

Web-based Interface to Census Interaction Data (WICID) WICID allows a user to build a query and extract origin- destination flow data from Censuses in 1981, 1991 and 2001 or from annual (mid-year) patient register/NHSCR flows from onwards

2001 Census A lot of analysis based on census data of different aspects of migration Aggregate net migration map shows key feature of UK migration pattern in But 181,476 potential origin-destination flows between 426 districts

Use of district classification system to analyse migration flow data, Groups of Vickers et al. classification Top 10 directional migration rates between Groups

Migration since 2001 How has the magnitude, composition and pattern of internal migration changed since 2001? Mid-year population estimates for Local Authority Districts prepared by ONS are very important for resource allocation ONS use patient register/NHSCR data as the basis of the internal migration component of the MYEs Need to verify relationship between these data and data from the Census if we want to feel comfortable about using the former for monitoring

Verification of NHS data against 2001 Census data for England and Wales Migration rates are for 376 Local Authority Districts (LADs) in

Level of inter-district migration, England and Wales, to

Changing patterns of internal migration at district level, England and Wales, all ages Change to

Changing patterns of internal migration at district level, England and Wales, ages Change to

Changing patterns of internal migration at district level, England and Wales, ages Change to

Net migration by age for Yorkshire and Humber, to

How do we monitor migration over time most effectively? The number of potential flows to analyse is quite large: 376 origins x 376 destinations x 8 ages = 1.13million potential flows Therefore, what more aggregate systems of spatial units might be appropriate for monitoring - Counties? - NUTS 2 regions? - Government Office Regions? Use a district classification

Net migration changes over time by district type, to Net flows are computed as the sum of net migration balances for districts in each category

District classification based on 2001 Census ‘migration’ variables 56 variables were selected from an original list of 5,559 taken from 2001 Census Special Migration Statistics (SMS) level 1 (District) tables Domains included: - In/out/within/no usual address migration rates for age, ethnicity, economic activity and long- term illness - Migration efficiencies (due to no suitable denominator) for socio-economic status, family status and housing tenure MATLAB used for K-means solution giving 8 clusters

Migration Classification 1 Coastal and rural retirement migrants 2 Sedentary middle-class Britain 3 Student towns 4 Intermediate single migrants 5 Constrained, working class, local Britain 6 Footloose, middle class, commuter Britain 7 Dynamic London 8 Successful family in-migrants Cluster names are provisional

Cluster 1: Coastal and rural retirement migrants Characterised by in-migrants and within-area migrants in the older age groups (45 and above) Younger in-migrants are very much underrepresented Migrants into these areas are from across the socio-economic spectrum, although the very high socio-economic groups are less common Migrants preferentially move into owner occupied accommodation and tend to be either or alone or in couples, far more than parent families

Cluster 2: Student towns Characterised by high levels of student in-migration, and young person within-area migration Non-household moving groups into privately rented accommodation are common in this cluster, as are non- family households and individuals moving into communal establishments – all characteristics of a student population In addition, non-white within- area migration is important, as is in-migration of economically inactive migrants

Net migration changes over time by district class, to

Average in-migration rates, Age group

Average out-migration rates, Age group

Net migration rates (per 1,000) for clusters, ages 0-15, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 16-19, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 20-24, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 25-29, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 30-44, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 45-59, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 60-74, Net migration rate per 1,000

Net migration rates (per 1,000) for clusters, ages 75+, Net migration rate per 1,000

Standardised migration ratios (excluding intra-cluster flows),

Standardised migration ratios (including intra-cluster flows),

Conclusions Internal migration involves around 1 in 10 of population every year – major determinant of population change Spatial pattern at district level dominated by metropolitan net migration gains and non-metropolitan net losses (though patterns vary by age) Some fluctuation over time since 2001 in level of migration – likely fall since onset of recession Major change in early 2000s was rise and fall in net losses from Greater London Bespoke migration classification provides a useful framework for analysing time-series migration patterns Clusters exhibit migration profiles with distinctive flow and age characteristics which can fluctuate over time

Some references Stillwell, J. (2006) Providing access to census-based interaction data in the UK: that’s WICID, The Journal of Systemics, Cybernetics and Informatics, 4(4): Dennett, A. and Stillwell, J. (2008) Population turnover and churn - enhancing understanding of internal migration in Britain through measures of stability, Population Trends, 134: Dennett, A. and Stillwell, J. (2009) Internal migration in Britain, , examined through an area classification framework, Population, Space and Place, Published online May DOI: /psp.554 Stillwell, J., Duke-Williams, O. and Dennett, A. (eds.) (2010) Technologies for Migration and Commuting Analysis: Spatial Interaction Data Applications, IGI Global, Hershey

Thank you Working Paper on classification: Details of CIDER available from: My contact