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.ukwww.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

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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

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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

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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

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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 AttainmentExtended 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

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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 101.2 Higher professional occupations 382 Lower managerial and professional occupations 783 Intermediate occupations 425 Lower supervisory and technical occupations 416 Semi-routine occupations 887 Routine occupations 74

Total 371

* Employees

Possible interesting variations within NS-SEC categories?

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Other Eng

MedicsUni teach

Publicans

Teachers (secondary)

Aux Nurses

Other teachers

Gardeners

Elec fitters

Food pro

Educ ass

Bar staff

Other misc

Metal mates

2030

4050

Mea

n G

CS

E S

core

1.1 1.2 2 3 4 5 6 7 Family Social Class

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).

Mean GCSE Score, Parents' SOC90 (large SOC groups)Illustrations of extreme occupations

GCSE Attainment Year 11

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YCS DataSecondary Teachers

Publicans

Count 1320 2225+ A*-C 78% 25%Mean No. A*-C 7.00 2.80Mean GCSE Score 49.05 29.64

McKnight and Elias (1998) 371 DatabaseSecondary Teachers

Publicans

Male Earnings Band £450-£500 £250-£300Female Earnings Band £350-£400 £150-£200Male Graduates in Occupation 85% 4%Female Graduates in Occupation 71% 1%

Regrettably the micro-data used to construct the 371 database is no longer available!

Working to reconstruct this information from summary 371 database filesWorking to construct similar measures from the Labour Force Survey

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 by their professional cultures and practices? 12

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)

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Microclass ReproductionMechanisms of Intergenerational Reproduction(Jonsson et al 2009 Table 1 p.986)

• Human capitalOccupation-specific skills (e.g. carpentry)

• Cultural capitalOccupation-specific cultures and tastes (e.g. aspirations, medicine, help with UCAS application)

• Social networksOccupation-specific networks (e.g. doing ‘the knowledge’, job interviews, internships)

• Economic resourcesFixed resources (e.g. farms, market stalls, business in general)

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Microclass Analysis

• The initial appeal is the prospect of clearer resolution regarding

1.Occupation-Specific Human Capital2.Occupation-Specific Cultural Capital3.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

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Examples of the Composition of Microclasses

Health Professionals Health Semi-Professionals220 Medical practitioners 222 Ophthalmic opticians221 Pharmacists / pharmacologists 340 Nurses223 Dental practitioners 341 Midwives224 Veterinarians 342 Medical radiographers

343 PhysiotherapistsWorkers in religion 344 Chiropodists292 Clergy 345 Dispensing opticians

347 Occupational and speech therapistsElementary and Secondary teachers 348 Environmental health officers233 Secondary school teachers 349 Other health associated professionals234 Primary school teachers235 Special education239 Other teaching (e.g. dance)

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Other Eng

MedicsUni teach

Publicans

Teachers (secondary)

Aux Nurses

Other teachers

Gardeners

Elec fitters

Food pro

Educ ass

Bar staff

Other misc

Metal mates

2030

4050

Mea

n G

CS

E S

core

1.1 1.2 2 3 4 5 6 7 Family Social Class

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).

Mean GCSE Score, Parents' SOC90 (large SOC groups)Illustrations of extreme occupations

GCSE Attainment Year 11

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35

40

45

50

Mea

n G

CS

E S

core

NS-SEC3Other (e.g.dance)

SecondaryPrimary

SpecialMicroclass

Source: SN5765;1990s YCS Cohorts.

Parents in teaching occupations

GCSE Attainment Year 11Mean GCSE Score, comprehensive school pupils

Microclass Analysis

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Least Squares Dummy Variable Models GCSE Score

No. Units Adjusted R2

NS-SEC (8 category) 8 .19SOC 90 units 369 .22ISCO 88 102 .21Microclass units 81 .21Controls: Cohort+Gender+Ethnicityn=55120

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

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Human Geography (Family Migration)

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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.

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.

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.

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 201122

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)

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Family Migration• Submitted to Journal of Epidemiology and Community HealthIn 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)

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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)!25

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!

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Parents / step parents(co-resident) Parents / step parents

(non-resident)

Older siblings

Household(resident)

Other Household(part-time / non resident)

Understanding youth transitions in the context of contemporary home and family life

Possible UKHLS (BHPS) data sources

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

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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

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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

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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

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