Vernon Gayle Professor of Sociology School of Applied Social Science University 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 1 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 2 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 3 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 4 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 5 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 6 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 7 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) 8 Exploring parental influences at occupational unit group (OUG) level National Statistics Socio-economic Classification (NS-SEC) NS-SEC No. of SOC90 Occupations* 1.1 Large Employers and higher managers 1.2 Higher professional occupations 2 Lower managerial and professional occupations 3 Intermediate occupations 5 Lower supervisory and technical occupations 6 Semi-routine occupations 7 Routine occupations 10 38 78 42 41 88 74 Total 371 * Employees Possible interesting variations within NS-SEC categories? 9 GCSE Attainment Year 11 Mean GCSE Score, Parents' SOC90 (large SOC groups) Illustrations of extreme occupations 50 Uni teach Medics Teachers (secondary) Other teachers 40 Other Eng Elec fitters Other misc Educ ass 30 Metal mates Publicans Aux Nurses Gardeners Food pro 20 Bar staff 1.1 1.2 2 3 4 5 Family Social Class 6 7 Mean for NS-SEC Class Source:1990s YCS Cohorts; Comprehensive school pupils. 121 larger SOCs; Pupils per SOC Mean 380; Min 101; Max 1836 (Nurses). 10 YCS Data Count 5+ A*-C Mean No. A*-C Mean GCSE Score Secondary Teachers 1320 78% 7.00 49.05 Publicans Secondary Teachers £450-£500 £350-£400 85% 71% Publicans 222 25% 2.80 29.64 McKnight and Elias (1998) 371 Database Male Earnings Band Female Earnings Band Male Graduates in Occupation Female Graduates in Occupation £250-£300 £150-£200 4% 1% 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 11 Microclass Analysis • There might be extra insights somewhere between ‘big class categories’ and ‘individual occupations’? • Exciting debate emerging • Punch up between heavyweights… For microclasses Grusky, 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 12 by their professional cultures and practices? 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) 13 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) 14 Microclass Analysis • The initial appeal is the prospect of clearer resolution regarding 1. Occupation-Specific Human Capital 2. Occupation-Specific Cultural Capital 3. 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 15 Examples of the Composition of Microclasses Health Professionals 220 Medical practitioners 221 Pharmacists / pharmacologists 223 Dental practitioners 224 Veterinarians Workers in religion 292 Clergy Elementary and Secondary teachers 233 Secondary school teachers 234 Primary school teachers 235 Special education 239 Other teaching (e.g. dance) Health Semi-Professionals 222 Ophthalmic opticians 340 Nurses 341 Midwives 342 Medical radiographers 343 Physiotherapists 344 Chiropodists 345 Dispensing opticians 347 Occupational and speech therapists 348 Environmental health officers 349 Other health associated professionals GCSE Attainment Year 11 Mean GCSE Score, Parents' SOC90 (large SOC groups) Illustrations of extreme occupations 50 Uni teach Medics Teachers (secondary) Other teachers 40 Other Eng Elec fitters Other misc Educ ass 30 Metal mates Publicans Aux Nurses Gardeners Food pro 20 Bar staff 1.1 1.2 2 3 4 5 Family Social Class 6 7 Mean for NS-SEC Class Source:1990s YCS Cohorts; Comprehensive school pupils. 121 larger SOCs; Pupils per SOC Mean 380; Min 101; Max 1836 (Nurses). 17 GCSE Attainment Year 11 Mean GCSE Score, comprehensive school pupils 35 40 45 50 Parents in teaching occupations NS-SEC3 Secondary Other (e.g.dance) Special Primary Microclass Source: SN5765;1990s YCS Cohorts. 18 Microclass Analysis Least Squares Dummy Variable Models GCSE Score NS-SEC (8 category) SOC 90 units ISCO 88 Microclass units No. Units 8 369 102 81 Adjusted R2 .19 .22 .21 .21 Controls: Cohort+Gender+Ethnicity n=55120 19 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 20 Human Geography (Family Migration) 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. 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. 21 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 22 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) 23 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 sociodemographic characteristics and school moves Childhood mobility associated with • overall subjective health • psychological distress • health behaviours • but not physical health (medical data) 24 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 25 • Nobel et al (2008) report no significant social class pattern (using NS-SEC)! 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! 26 Understanding youth transitions in the context of contemporary home and family life Other Household (part-time / non resident) Household (resident) Parents / step parents (co-resident) Older siblings Parents / step parents (non-resident) Possible UKHLS (BHPS) data sources 27 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 28 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 29 Developing the Longitudinal Studies Centre – Scotland • Maintaining continuity and recognising opportunities • • • • Generating research income • • • • Flagship product – Scottish Longitudinal Study Synergies with e-Social Science, data linkage, ADLS, secure data etc. Always room for methodological work (missing data) 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 30 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 31