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
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Summary of Academic History
and Foundations
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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
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Research based on detailed empirical analyses
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Large-scale social surveys (especially longitudinal data)
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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
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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
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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
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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)
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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
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?
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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).
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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
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Microclass Analysis
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There might be extra insights somewhere between ‘big
class categories’ and ‘individual occupations’?
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Exciting debate emerging
•
Punch up between heavyweights…
For microclasses Grusky, Weeden and Jonsson
Against Goldthorpe and Erikson
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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
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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)
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Microclass Reproduction
Mechanisms of Intergenerational Reproduction
(Jonsson et al 2009 Table 1 p.986)
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Human capital
Occupation-specific skills (e.g. carpentry)
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Cultural capital
Occupation-specific cultures and tastes
(e.g. aspirations, medicine, help with UCAS application)
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Social networks
Occupation-specific networks
(e.g. doing ‘the knowledge’, job interviews, internships)
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Economic resources
Fixed 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 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
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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
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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).
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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.
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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
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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)
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.
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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
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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 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)
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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
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• 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!
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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
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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?
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Enthusiasm, commitment, energy
Methodological skills
‘Sociological insights’
Inter and multidisciplinary researcher expertise
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Research and Teaching
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Research led teaching
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Research intensive university
UG teaching is extremely important (growing postgraduates)
Methodological teaching
Substantive teaching researching with large-scale datasets
Research supervision
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Locate within Population, Health and Welfare Group
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Continuing to work within the ESRC Centre for Population Change
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Migration work
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Developing the Longitudinal Studies Centre – Scotland
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Maintaining continuity and recognising opportunities
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Generating research income
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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…
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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
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An increasingly devolved political climate?
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Scottish data analytical expertise
Scottish data housing
The Scottish Essex?
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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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