Champion, Tony

Duke-Williams, Oliver

English

UCL Discovery

Migration and the ‘left-behindplaces’Tony Champion (CURDS, Newcastle) greatly aided by Oliver Duke-Williams (UCL)Paper presented at the one-day meeting on ‘Stronger Towns: What can the Census tell us?’ held at the Institute of Health Informatics, London, 19 July 2019Left-behind places: a brief history• Government concern about ‘LBP’ dates at least as far back as the aftermath of the Great Depression and the Jarrow March, notably the ‘Special Areas’ Act of 1934• Interventions continued through the post-war period, e.g.‘Development Areas’, urban regeneration initiatives, and support for coastal communities and former mining towns• These were paralleled by a great amount of academic and media interest, with the latest concerns neatly summarised by Rodrigues-Pose’s (2018) view that Brexit and the rise of popularism constitute ‘the revenge of the LBP’• Latest government initiative is the Stronger Towns Fund (STF), announced March 4th, with £1.6b for 2019-2026, of which £1b is to be allocated on ‘a needs-based formula targeting places with weaker economies, lower incomes and fewer skills’• Aims today are to identify the specific places which the STF is likely to be targeting and show how the ONS Longitudinal Study (ONS-LS) can help to study their migration patterns The ONS Longitudinal Study• A 4/365 (c 1.1%) sample of persons present in censuses in England and Wales, 1971-2011. The sample birthdates are not disclosed• Data consists of (de-identified) microdata records• Sample members are linked across censuses• Data for other persons in the household are also available; these are not linked across censuses• Most census records are available with detailed coding; a few fields have restricted access• Around 513,000 persons in the 1971 file, growing to 582,000 in the 2011 sample; over 1 million individuals present across the whole seriesExpectations from previous work• My own interest dates from 1985 with the first of a series of reports on ‘Booming Towns’ (with Anne Green), which adopted the (US) Places Rated Almanac) in ranking CURDS Local Labour Market Areas using an index based on combination of indicators – result:   1. Winchester, Hants; through to… 280. Consett, Co Durham• Most recent CURDS study (Unequal Growth, for JRF, 2017) ranked the UK’s largest 74 cities on ‘index of relative decline’: highest was Rochdale, then Burnley, Bolton, Blackburn, Hull, Grimsby, Dundee, Middlesbrough, Bradford, and Blackpool• ONS also active, notably its Major Towns and Cities report (March 2016) on the 112 urban areas with >75,000 people: - lowest median house price: Burnley, Oldham, Bradford, Halifax- most deprived (IMD): Oldham, West Bromwich, Liverpool, Walsall- most net out-commuting: Sutton Coldfield, Chatham, South Shields• Latest from ONS is Understanding Towns (July 2019), which covers 1,186 urban areas with 5,000-225,000 people: 91 >75,000, 347 20,000-75,000 and 748 5,000-20,000, with data on 5 indicators Our approach in outline• Create a ranking of ‘places’ via a composite index based on the criteria set out in the MHCLG’s announcement of the Stronger Towns Fund (STF), see: https://www.gov.uk/government/publications/stronger-towns-fund/stronger-towns-fund-questions-and-answers• Group the places into deciles and examine their aggregate  characteristics, with a particular focus on the lowest-ranking of the 10 categories, i.e. the places most likely to qualify for the £1b pot• As regards studying migration behaviour, focus on address changing between these 10 categories, seeing what % of residents in 2001 were in the same decile in 2011 and, if different, in which other one• So far, we have looked at 4 variables: gender, age, ethnicity (as reported in 2011) and housing tenure (as reported in both 2001 and 2011 to detect tenure change)  Creating our STF index I• According to the MHCLG’s announcement on the STF, the formula is being ‘based on a combination of productivity, income, skills, deprivation measures and the proportion of people living in towns’ – so presumably using data for individual Local Authorities on these five dimensions• So, to construct an index for England’s Local Authorities, we have gathered data on these five dimensions, specifically:• Productivity: sub-regional estimates (NUTS3 re-cast as LA)https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/labourproductivity/articles/regionalandsubregionalproductivityintheuk/february2018Lower productivity = lower rank• Income: estimates of gross disposable household incomehttps://www.ons.gov.uk/economy/regionalaccounts/grossdisposablehouseholdincome/datasets/regionalgrossdisposablehouseholdincomegdhibylocalauthorityintheukLower household income = lower rankCreating our STF index II• Skills: aggregate 2011 Census countsProportion of persons with no qualifications or level 1 qualifications as highest qualificationsHigher proportion of these persons = lower rank• Deprivation: IMD 2015Proportion of LSOAs in lowest decile, then proportion in second decile etc...Higher proportion of LSOAs in lowest decile = lower rank• Proportion living in townshttps://www.gov.uk/government/statistics/2011-rural-urban-classification-of-local-authority-and-other-higher-level-geographies-for-statistical-purposesProportion of the 2011 population living in 'rural towns', then proportion in 'rural other', then 'other urban', then 'large urban', then 'major urban'Higher proportion in rural towns etc = lower rank  Creating our STF index III• The rank scores for each dimension were summed to give an (equally weighted) overall score• Then the overall score was converted to a rank value, from which rank deciles were derived• These decile scores were then attached to data from the ONS Longitudinal Study by: - harmonising the LA area codes and then attaching deciles to 2011 districts - attaching 2011-type area codes to 2001 districts (replicating where districts were merged in the 2011 Census output), and then attaching the decile codes to the 2001 districtsResult: the LA membership of the ‘weakest’ decileNotes: Ranked, with weakest = 1. Regional location in brackets. Mainly NE North East, NW North West, YH Yorkshire & Humber; but also some WM West Midlands, EM East Midlands, EE East and one SE South East. Mainly old industrial & mining, some coastal and/or remoter rural Sources: the slides ‘Creating our STF index I and II’, see above1  East Lindsey (EM) 9    Doncaster (YH) 17  Blackburn with D (NW) 25  Burnley (NW)2  Redcar & C’land (NE) 10  Bassetlaw (EM) 18  Oldham (NW) 26  Mansfield (EM)3  Tendring (EE) 11  Bradford (YH) 19  N Lincolnshire (YH) 27  Hyndburn (NW)4  County Durham (NE) 12  Wakefield (YH) 20  St. Helens (NW) 28  Wigan (NW)5  Barnsley (YH) 13  NE Lincs (YH) 21  Bolsover (EM) 29  Knowsley (NW)6  Great Yarmouth (EE) 14  Walsall (WM) 22  Blackpool (NW) 30  S Holland (EM)7  Copeland (NW) 15  Rotherham (YH) 23  Kingston-u-Hull (YH) 31  W Lindsey (EM)8  Stoke-on-Trent (WM) 16  Allerdale (NW) 24  Swale (SE) 32  Pendle (NW)Our data on (10-year) migrationNotes: This is the standard format used for our data extraction from ONS-LS, i.e. an origin-destination matrix based on 2001 and 2011 decilesThis particular table is for all LS members identified in both the 2001 and 2011 Censuses = 396,683Similar tables have been generated for males, females, 16-25. 26-45, 46-64, 65+, white, other ethnicity, owner occupier, social renter, private renter All these tables have publication clearance from ONS and all the results that follow are based entirely on these outputs2001 decile2011 decileTotal 20011 2 3 4 5 6 7 8 9 101 41882 1020 1602 778 484 448 454 231 204 210 473132 1000 37412 1187 940 630 415 719 377 265 258 432033 1584 1218 47770 1219 836 663 947 574 365 487 556634 722 913 1017 31499 954 743 792 676 370 397 380835 440 623 839 972 28683 569 963 602 457 408 345566 384 430 721 800 568 23612 825 820 504 407 290717 432 695 826 940 962 1042 31849 946 1030 702 394248 298 475 615 814 740 1125 1223 27368 1289 797 347449 312 471 572 649 773 896 1586 1538 31124 2259 4018010 217 405 464 475 526 620 1038 1127 2948 26626 34446Total 2011 47271 43662 55613 39086 35156 30133 40396 34259 38556 32551 396683What % of a decile’s 2011 residents had been living in a different decile in 2001?Decile 1 (weakest) is the least attractive for newcomers; decile 6 the most attractive with rate dropping off for strongest deciles – places that are harder to enter?Source: Calculated from ONS Longitudinal Study tables cleared for publication% of decile's 2011 residents moving in from a different decile in 2001 (newcomers)0.05.010.015.020.025.01 2 3 4 5 6 7 8 9 10decile (1 = weakest; 10 = strongest)per centWhat % of a decile’s 2001 residents had moved out to a different decile by 2011?Decile 1 (weakest) lost the fewest residents, with fairly regular increase in leaving rate with rising strength – reflects the greater mobility of stronger placesSource: Calculated from ONS Longitudinal Study tables cleared for publication% of decile's 2001 residents moving out to a different decile in 2011 (leavers)0.05.010.015.020.025.01 2 3 4 5 6 7 8 9 10decile (1 = weakest; 10 = strongest)per centWhat net change in the deciles’ populations due to these leavers and newcomers?% newcomers and leavers, 2001-2011, by decile0.05.010.015.020.025.01 2 3 4 5 6 7 8 9 10decile  (1 = weakest; 10 = strongest)per cent% newcomers % leaversDeciles 1-3 (weakest) have the lowest 10-year turnover and net change; 4-7 grow fastest, and 8-10 (the strongest including London Boroughs) lose population Source: Calculated from ONS Longitudinal Study tables cleared for publicationHow do the various age groups contribute to this net migration 2001-2011?Decile 1 (in red) loses 16-25s (aged 6-15 in 2001) but gains 46+, as does decile 2; the strongest (i.e. with highest living costs) lose older people to all deciles except 3 (decile with university towns?)Source: Calculated from ONS Longitudinal Study tables cleared for publicationNet change in residents, 2001-2011, by age group and decile-1000-800-600-400-2000200400600800100016-25 26-45 46-64 65+Age in 20111 2 3 4 5 6 7 8 9 10Those who left decile 1 after 2001: in which decile were they living in 2011? Destination decile of those leaving decile 1, 2001-20110.05.010.015.020.025.030.02 3 4 5 6 7 8 9 10% decile 1 leaversdecile in 2011The vast majority moved to the 3 next weakest deciles (2-4), with 8-9% to each of the 3 next stronger (5-7) and fewest to the 3 strongest (8-10, ca 4% each). Partly due to housing cost etc. But also, most migration tends to be short-distance to surrounding (similar) places. Source: Calculated from ONS Longitudinal Study tables cleared for publicationWhere were those leaving decile 1 living in 2011, by characteristic?.0% 20% 40% 60% 80% 100%All16-2526-4546-6465+MaleFemaleWhiteNon-whiteOwner occupierSocial renterPrivate rentercharacteristic in 2011% of those leaving decile 1, 2001-20112 3 4 5 6 7 8 9 10decile in 2011Top bar is same data as on previous slide (for benchmarking). Those aged 65+ and social renters more likely to move to decile 2 (next weakest) than average. 16-25s and private renters (as of 2011) less likely to move to decile 2, but more so to decile 3. Source: Calculated from ONS Longitudinal Study tables cleared for publicationChange in housing tenure, 2001-2011, by whether staying in same decile or moving to a weaker or stronger one0% 20% 40% 60% 80% 100%Owner occSocial rentPrivate rentOwner occSocial rentPrivate rentOwner occSocial rentPrivate rentdecile downdecile samedecile upOwner occ Social rent Private rentTenure in 2011Tenure in 2001Making use of data for start of period as well as at end, for a characteristic that can change over time especially if moving home. Note the bigger shift from private renting for moving to lower or higher decile than staying in same one. More stay owners if moving up than down.Source: Calculated from ONS Longitudinal Study tables cleared for publicationFindings from an initial look at LS-ONS migration data from a STF perspective• The focus here has been on ONS-LS members moving between deciles of a LA-based Stronger Towns Index between 2001 and 2011• The weakest three deciles contained the fewest 2001-2011 incomers from the other deciles, but also had the fewest leavers, reflecting their less migratory populations• The weakest two deciles recorded big net losses of 16-25s, while the three strongest made big losses of those aged 46+, the latter associated with retirement and family reasons• The weakest decile’s leavers moved mainly to the three next weakest deciles, with 65+ & social renters focusing on decile 2 and 16-25s and private renters focusing on decile 3• In terms of change in tenure between 2001 and 2011, more people who started as private renters became owners if they moved to a stronger decile than to a weaker one or stayed putBenefits of using the ONS-LS to study migration• Its linked census data allows the analysis of 10-year address changing, allowing long-term trends to be identified, e.g. comparing 2001-2011 with 1991-2001 and back to 1971-81• Classification codings (like the Stronger Towns Index used here) can be attached to the relevant geographies stored for LS members’ usual addresses at each census• In using the linked census records, migration can be analysed in terms of people’s ‘starting’ characteristics (cf the migration data from each census which has just the after-move details)• As a result, people can be classified in terms of the changes in people’s characteristics over the decade, e.g. whether movers changed, e.g., their qualifications, occupation, housing tenure• The next step would be to replicate the ‘escalator region’ approach but concentrating on the weakest places and seeing how well their leavers fared compared to their stayers  Acknowledgements & disclaimer The data presented in this paper has been produced by ONS LS project 0300411.The permission of the Office for National Statistics to use the Longitudinal Study is gratefully acknowledged, as is the help of the Centre for Longitudinal Study Information & User Support (CeLSIUS). CeLSIUS is supported by the ESRC Census of Population Programme (Award Ref: ES/K000365/1). The author alone is responsible for the interpretation of the data.This work contains statistical data from the ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. The work uses research datasets which may not exactly reproduce National Statistics aggregations.Migration and the ‘left-behindplaces’Tony Champion Centre for Urban & Regional Development Studies (CURDS), Newcastle University tony.champion@ncl.ac.uk 

Migration and the 'left-behind places'

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Migration and the 'left-behind places'

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