Vernon Gayle Professor of Sociology School of Applied Social Science University of Stirling

1. Vernon GayleProfessor of Sociology School of Applied Social ScienceUniversity of Stirling - A summary of your academic history and foundations - Where you would like to take your research in future - How you will build your research within the context of St Andrews vernon.gayle@stir.ac.uk www.staff.stir.ac.uk/vernon.gayle/ www.dass.stir.ac.uk/staff/showstaff.php?id=28

2. Summary of Academic History and Foundations Sociologist by training In an Applied Social Science Department since 1994 Working alongside non-survey based researchers Inter and multidisciplinary researcher Geographers, economists, statisticians, computer scientists, health Research based on detailed empirical analyses Large-scale social surveys (especially longitudinal data) Multivariate statistical analysis Emphasis on advanced techniques

3. Ongoing Collaborations with St Andrews Secondment to the Longitudinal Studies Centre – Scotland (2003-6) Galvanise the already successful set of collaborative arrangements and rapidly make substantial progress Boyle, Graham, Feng, Feijten and Flowerdew Work in longitudinal data, migration, fertility and family life 5 ESRC funded awards (research and knowledge exchange); 6 publications; 13 conference presentations; 4 research reports; 6 ESRC funded consultancies

4. Longitudinal Data Longitudinal data are not a panacea For many analyses cross-sectional data are suitable Most analyses can be improved when longitudinal data are incorporated I argue that some research questions require longitudinal data Flows into and out of poverty The effects of family migration on woman’s subsequent employment activities Evaluating policy interventions Investigating ‘individual’ development

5. Moving to St Andrews Developing spatial elements in my work Intellectual ambition is to develop suitable collaborations with social geographers in order to provide more comprehensive analyses of both the temporal and the spatial elements of contemporary social life

6. Some Current Research Areas Sociological / Educational research in social stratification youth transitions, education, occupations Research in human geography family migration, ESRC Centre for Population Change Methodology better communicating results, quasi-variance, missing data methods

7. Some Other Current Research Areas Modelling ordinal panel data Gayle (1996); ESRC NCRM; attitudinal data; bi and tri variate outcome random effects models (correlated error structures) Data management ESRC NCeSS Node; managing, enabling survey data; constructing measures; grid technology; digital social research Knowledge transfer/capacity building ESRC RM Programme, RDI Phase 1 & 2, ESRC AQMeN; training researchers; building capacity; statistical modelling; longitudinal data analysis; ONS; Scottish Gov; Local Authorities

8. Parental Occupations and Filial Attainment Extended analyses of the Youth Cohort Study of England and Wales Overall trend Increasing proportions getting 5+GCSEs (A*-C) Increasing mean number of A*-C grade GCSEs Increasing mean GCSE points score Gender Female pupils outperforming male pupils Ethnicity Some groups doing better than white pupils (e.g. Indians) Other groups doing worse (e.g. blacks) Parental Occupation Observable gradient Lower levels of GCSE attainment from those pupils with less occupationally advantaged parents Sensitivity analysis of 9 popular occupational measures (Adj. R2 =.15 through to .20)

9. Exploring parental influences at occupational unit group (OUG) levelNational Statistics Socio-economic Classification (NS-SEC) NS-SEC No. of SOC90 Occupations* 1.1 Large Employers and higher managers 10 1.2 Higher professional occupations 38 2 Lower managerial and professional occupations 78 3 Intermediate occupations 42 5 Lower supervisory and technical occupations 41 6 Semi-routine occupations 88 7 Routine occupations 74 Total 371 * Employees Possible interesting variations within NS-SEC categories?

11. Regrettably the micro-data used to construct the 371 database is no longer available! Working to reconstruct this information from summary 371 database files Working to construct similar measures from the Labour Force Survey

12. Microclass Analysis There might be extra insights somewhere between ‘big class categories’ and ‘individual occupations’? Exciting debate emerging Punch up between heavyweights… For microclassesGrusky, Weeden and Jonsson Against Goldthorpe and Erikson Jonsson et al 2009 AJS; Grusky and Weeden (2005, 2006) Between 8 categories and 371 unorganised occupational unit groups, could there be 80-120 microclasses defined by their professional cultures and practices?

13. Microclass Analysis ‘Microclass regime —The microclass approach shares with the big-class model the presumption that contemporary labor markets are balkanized into discrete categories, but such balkanization is assumed to take principally the form of institutionalized occupations (e.g., doctor, plumber, postal clerk) rather than institutionalized big classes (e.g., routine nonmanuals, proprietors)’ (Jonsson et al 2009 pp.982-983)

14. Microclass Reproduction Mechanisms of Intergenerational Reproduction (Jonsson et al 2009 Table 1 p.986) Human capital Occupation-specific skills (e.g. carpentry) Cultural capital Occupation-specific cultures and tastes (e.g. aspirations, medicine, help with UCAS application) Social networks Occupation-specific networks (e.g. doing ‘the knowledge’, job interviews, internships) Economic resources Fixed resources (e.g. farms, market stalls, business in general)

15. Microclass Analysis The initial appeal is the prospect of clearer resolution regarding Occupation-Specific Human Capital Occupation-Specific Cultural Capital Other Occupation-Specific Mechanisms First attempt (that we are aware of) to construct a British microclass scheme Example (from Gayle and Lambert 2011) http://www.staff.stir.ac.uk/vernon.gayle/documents/gayle_lambert_rc28_v1.pdf

16. Examples of the Composition of Microclasses Health ProfessionalsHealth Semi-Professionals 220 Medical practitioners 222 Ophthalmic opticians 221 Pharmacists / pharmacologists 340 Nurses 223 Dental practitioners 341 Midwives 224 Veterinarians 342 Medical radiographers 343 Physiotherapists Workers in religion 344 Chiropodists 292 Clergy 345 Dispensing opticians 347 Occupational and speech therapists Elementary and Secondary teachers 348 Environmental health officers 233 Secondary school teachers 349 Other health associated professionals 234 Primary school teachers 235 Special education 239 Other teaching (e.g. dance)

19. Least Squares Dummy Variable Models GCSE Score Microclass Analysis Controls: Cohort+Gender+Ethnicity n=55120

20. Microclass Analysis First attempt to construct a British microclass scheme Extra explanatory power (for GCSE attainment) questionable The initial appeal was the prospect of clearer resolution regarding Occupation-specific human and cultural capital and occupation specific mechanisms Family migration and microclasses / beyond ‘big classes’ Mobility / immobility of microclasses Trailing spouses License to practice Geographical distribution of microclasses Unemployment at microclass level

21. Boyle, P., Kulu, H., Cooke, T., Gayle, V. and Mulder, C. (2008) ‘The Effects of Moving on Union Dissolution’, Demography, 45(1), pp. 209-22. Human Geography (Family Migration) Boyle, P., Feng, Z. and Gayle, V. (2009) ‘A New Look at Family Migration and Women’s Employment Status’, Journal of Marriage and Family, 71, pp. 417-431. Gayle, V., Boyle, P., Flowerdew, R. and Cullis, A. (2008) ‘Exploring the relationship between family migration and social stratification through the investigation of women’s labour market experiences in contemporary Britain’,International Journal of Sociology and Social Policy (Special Issue), 28 (7/8), pp. 293-30.

22. Family Migration ESRC Centre for Population Change Collaboration with Elspeth Graham and Marina Shapira (GROS) Greatly extends our previous BHPS based research Huge data preparation exercise Data in an advanced state of readiness Combining detailed migration information with fertility, partnership, employment and occupational data Paper accepted Understanding Society / BHPS Conference July 2011

23. Family Migration Colleagues MRC/CSO Social and Public Health Sciences Unit, Glasgow The relationship between childhood residential mobility and health in the UK is not well established Research elsewhere suggests that frequent childhood moves may be associated with poorer health outcomes and behaviours Comparison of people in the West of Scotland who were residentially stable in childhood with those who had moved in terms of a range of health measure (West of Scotland Twenty-07 Study)

24. Family Migration Submitted to Journal of Epidemiology and Community Health In a nutshell … Risk of poor health was elevated in adolescence and adulthood with increased residential mobility in childhood, after adjusting for socio-demographic characteristics and school moves Childhood mobility associated with overall subjective health psychological distress health behaviours but not physical health (medical data)

25. Methodology Missing data (item non-response) enduring survey problem Practical issue - Youth Cohort Study young people being asked about their parents occupations In the 1990s cohorts approximately 12% of pupils with missing parental occupation data Nobel et al (2008) testing pupils with the YCS question and checking with parental interview data 60% of young people correctly reported parents’ occupations at 4 digit Occupational Unit Group (e.g. 2111 Chemist) Disappointingly only 74% managed it at the 1 digit level – either they know exactly or they don’t know at all Nobel et al (2008) report no significant social class pattern (using NS-SEC)!

26. Missing Data & Multiple Imputation Can we get further using some of the recent insights from the missing data and multiple imputation literature? Carpenter & Bartlett (LSHTM), Goldstein (Bristol) Multiple imputed datasets (creation and analysis) Creating imputations by chained equations (ice) in Stata (n=64K not 55K) Results are promising Important first step, our focus was missingness on parental social class, but original models were underestimating ethnicity effects Richer (congenial) models for imputation Breakthrough is fitting survey weighted models for imputation Compared results with other estimation techniques (e.g. Realcom) We are looking into a generalisation to multilevel framework Application to spatially clustered data!

27. Understanding youth transitions in the context of contemporary home and family life Household (resident) Other Household (part-time / non resident) Parents / step parents (co-resident) Parents / step parents (non-resident) Older siblings Possible UKHLS (BHPS) data sources

28. How I will build my research in the St Andrews context Developing spatial elements within my work Intellectual ambition for more comprehensive analyses of both the temporal and the spatial elements of contemporary social life What do I bring? Enthusiasm, commitment, energy Methodological skills ‘Sociological insights’ Inter and multidisciplinary researcher expertise

29. Research and Teaching Research led teaching Research intensive university UG teaching is extremely important (growing postgraduates) Methodological teaching Substantive teaching researching with large-scale datasets Research supervision Locate within Population, Health and Welfare Group Continuing to work within the ESRC Centre for Population Change Migration work

30. Developing the Longitudinal Studies Centre – Scotland Maintaining continuity and recognising opportunities Flagship product – Scottish Longitudinal Study Synergies with e-Social Science, data linkage, ADLS, secure data etc. Always room for methodological work (missing data) Generating research income ESRC application YCS / BHPS Youth latent variables Top secret GTC/Scot Gov Teachers Panel Study (occupation and geography) Youth transitions contemporary home and family life (ESRC application) Growing unregulated markets… Enhanced knowledge exchange and capacity building Training in survey data analysis; longitudinal methods; data management Current ESRC Researcher Development Initiative call

31. Developing the Longitudinal Studies Centre – Scotland An increasingly devolved political climate? Scottish data analytical expertise Scottish data housing The Scottish Essex? Institute of Social and Economic Research, University of Essex UK Longitudinal Studies Centre MISOC Research Centre on Micro-social Change UK Data Archive