1. Using the Special Migration Statistics for research on human capital flows Tony Champion Paper presented to the Census 2011: Impact and Potential conference, at the University of Manchester, 7-8 July, 2011 Acknowledgements: The paper draws on work carried out with Mike Coombes, Simon Raybould and Colin Wymer and funded by the Joseph Rowntree Foundation. The 2001 Census SMS data was provided on CD by ONS and is Crown copyright. Introduction to the SMS 2001 Results on human capital flows Looking forward to 2011

2. Introduction to the SMS 2001 Only one of several 2001 Census aggregate datasets containing migration data: Key Statistics and Univariate Statistics Standard Tables Census Area Statistics Standard Theme Tables Origin-Destination Statistics = SMS But the SMS are the most valuable for migration analysis because of being for (1) flows between identified places; and (2) a number of migrants characteristics …

3. Tables in 2001 SMS Variables (P Person; MGRP Moving Group Ref Person Level 1 (district)Level 2 (ward) Table Count Age (P) MG101*75 MG201*51 Family status (P) MG102*54 Ethnicity (P) MG103*24 MG203*9 Limiting illness (P) MG104**84 Economic activity (P) MG105*42 Moving groups in households MG10616 MG2024 Tenure (MGRP) MG10732 MG2058 Economic activity (MGRP) MG108*336 NS-SeC (MGRP) MG109*288 MG20424 Gaelic/Welsh/Irish (P) MG110*36 Level 3 (output area): M301 Age* with 12 counts * by gender, ** by age

4. Research on human capital flows for GB cities Cities are increasingly being seen as drivers of regional economic growth, following a prolonged period of economic restructuring and population deconcentration Urban renaissance policies (e.g. Urban Task Force, 1999) aiming for economic regeneration, but so far a very uneven impact (SOECR 2006; Champion and Townsend, 2011) The knowledge economy is regarded as vital to growth both nationally and sub-nationally, with the availability of high- quality human capital being a key factor (Florida, 2002) Hence the importance of attracting and retaining people in more skilled occupations, and of the question: How well are Britains larger cities managing to do this? Project for JRF using SMS MG109 data on groupings of NS- SeC of Moving Group Reference Person aggregated to best fit of 27 large cities …

5. The 27 cities Focus on the Primary Urban Areas in grey the rest of the city regions (RCRs) in white

6. NS-SEC groupings of MG109 categories 1.1 Large employers & higher managerial Higher M&P 1.2 Higher professional 2 Lower managerial & professional Lower M&P 3 Intermediate Intermediate 4 Small employers & own account workers 5 Lower supervisory & technical Low 6 Semi-routine 7 Routine L15 Full-time students FT Students L14.1 Never worked Other unclassified L14.2 Long term unemployed L17 Not classifiable for other reasons

7. Within-UK migration, of MGRPs classified by NS-SeC at the census, for 27 cities combined NS-SeC of MGRPs at CensusInflowsOutflowsIn/out ratio All MGRPs389,902399,3680.976 Full Time Students76,31845,5271.676 Other unclassified31,33145,4720.689 All classified MGRPs282,253308,3690.915 Higher M&P67,16269,5150.966 Lower M&P99,404106,2890.935 Intermediate54,21060,8280.891 Low61,47771,7390.857 Source: calculated from 2001 Census SMS Table MG109

8. In/out ratio of within-UK migration of MGRPs classified by NS-SeC at the census, for 27 cities combined More out than in Unity = in/out balance More in than out Source: calculated from 2001 Census SMS Table MG109

9. Ditto, deviation from unity (i.e. from 1.0 = in/out balance): Source: calculated from 2001 Census SMS Table MG109

10. By 3 size groups: Source: calculated from 2001 Census SMS Table MG109

12. Ditto, for 9 largest cities and just longer-distance flows Source: calculated from 2001 Census SMS Table MG109

13. Classification of cities based on relative performance on longer-distance migration measures 2000-01 and immigration rate 2001-2003 TypeCities GatewayLondon Reading Brighton Stronger Bristol Derby Edinburgh Leeds Norwich Northampton Preston Portsmouth Moderate Bradford Glasgow Manchester Newcastle Nottingham Plymouth Sheffield Southampton Weaker Birmingham Cardiff Coventry Hull Leicester Liverpool Middlesbrough Stoke

14. In/out ratios of flows between cities and the rest of their city regions by longer-distance migration city types Source: calculated from 2001 Census SMS Table MG109 Balance

15. City types' Higher M&P in/out ratios for migration exchanges with London Source: calculated from 2001 Census SMS Table MG109 Balance

16. Plus similar analyses for 27 other potential determinants: demographic, cultural, socio-economic, housing, environmental

17. So, are Britains larger cities attracting/retaining more skilled people? Key findings: The 27-city aggregate picture for Higher M&Ps is quite encouraging: neutral is better than previous Censuses have suggested Yet just 4 of 27 cities had Higher M&P in/out ratios of >1.0 (London, Brighton, Derby, Northampton), with only 4 more coming close (Edinburgh, Reading, Manchester, Bristol) Even so, as expected, stronger performance is associated with positive labour market factors (especially local job growth and a more graduate-intensive workforce) There are also positive associations with some non-labour market factors, notably same-sex couples, people with no religion, high- status residents and White residents Factors linked to more positive in/out ratios between cities and the rest of their regions echo those for their longer-distance migration, i.e. a double whammy of net outflows for the weakest cities Key feature is the dominance of London: very positive in/out ratio for Higher M&Ps overall and especially a strong net inflow of Higher M&Ps from other cities (presumably including recent graduates)

18. Relevance to policy and future research These sorts of findings have generated much policy interest, with the results of this and similar studies being fed into both central and local government (e.g. DCLG, EMDA, MIER, TWCR) Key policy questions include: Can a more widespread urban renaissance be generated? How can more cities attract/keep the Higher Managerial & Professional? Given the correlation results, can quality-of-life advantages counter persistent regional imbalance in economic growth? Are the weaker cities to face further housing-market failure because of the double whammy of losing people to other parts of their own city regions as well as to other cities? Given that students moving to university boost most cities populations, what can be done to improve graduate retention in provincial cities? Clearly, questions to which the Census can provide only partial answers, BUT the 2011 Census provides an important opportunity to see how urban renaissance policies and other measures have altered the situation since the 2000-2001 migration year

19. Hopes for the 2011 Census in this context Direct comparisons with the 2001 Census results will be impossible because of certain changes, mostly for the better (and NB: given the lack of comparability, plan for the best possible for 2011 output) The district geography of the SMS1 has altered, most notably with the creation of several unitary counties (could there be a more standardised geographical basis – or just use pre-2009 admin geography?) The 2011 SMS data will not be subject to Small Cell Adjustment (good that a less damaging form of disclosure control will be used) Migrants with no usual address one year ago will have indicated their exact whereabouts (improvement on 0.5m just ticking a box in 2001) Migrants whose usual address one year ago was for where they were a student in termtime will have ticked a box, distinguishing them from those already in work (improvement if this can be factored into output – and the best option given no question on economic-activity-1-year-ago ) One final improvement hoped for from the consultations for ODS output (starting later this year): additional* SMS1 and SMS2 tables on NS-SeC for all persons i.e. including non-RP members of Moving Groups and people in communal establishments (*or possibly substitute?)

20. Using the Special Migration Statistics for research on human capital flows Tony Champion tony.champion@ncl.ac.uk