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Using EseC to look across and within classes. Workshop on Application of ESeC Lake Bled, 29-30 June 2006 Eric Harrison & David Rose ISER, University of Essex. Purposes of Paper. Replicate initial analysis using the new three digit matrix (‘Euroesec’) - PowerPoint PPT Presentation
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Using EseC to look across and within classes
Workshop on Application of ESeC
Lake Bled, 29-30 June 2006
Eric Harrison & David Rose
ISER, University of Essex
Purposes of Paper
• Replicate initial analysis using the new three digit matrix (‘Euroesec’)
• Explore new variables now available in round two of the European Social Survey
• Trial analysis using the draft Socio-economic groups (SEGs)
European Social Survey
• Rapidly becoming primary European dataset:– A more all-purpose instrument than LFS, with numerous socio-
political attitude measures– A more precise set of information for constructing ESeC than
ECHP and many more countries
• Two rounds now available (Round 3 in progress)• Round 1: 22 countries, 42,359 cases• Round 2: 24 countries, 45,681 cases (Italy still to
deposit)• Most or all of the information needed to make an ESeC,
i.e. 3 or 4 digit ISCO (e.g. French R2), employment status and supervision questions)
Sample Sizes in ESS 1 & 2
0 500 1000 1500 2000 2500 3000 3500
United Kingdom
Ukraine
Switzerland
Sweden
Spain
Slovenia
Slovakia
Portugal
Poland
Norway
Netherlands
Luxembourg
Italy
Israel
Ireland
Iceland
Hungary
Greece
Germany
France
Finland
Estonia
Denmark
Czech Republic
Belgium
Austria
co
un
try
achieved n
R2
R1
Three Performance Targets for EseC
• Does it work? Can it be operationalized?
• Can it measure what it purports to measure, over and over again?
• Does it discriminate and structure with regard to predicting values of related variables?
EseC Distributions in ESS 1&2
0
5
10
15
20
25
%
Largeemployers,
higher mgrs &professionals
Low er mgrs &professionals,
highersupervisory &technicians
Intermediateoccupations
Small employersand self-
employed -non-agriculture
Small employersand self-
employed -agriculture
Low ersupervisors and
technicians
Low er salesand service
Low er technical Routine
Class
R1
R2
ALL
The Treatment of Employment Status in R2
• Self-employed, supervisors, employees• Employment relation variable – France and
Hungary inserted extra categories. These can be collapsed back into the main dataset
• No problem with self-employed in R2:– Family workers (small N) treated as employees
• Supervision – remains ambiguous in social surveys
• Management – rely on ISCO codes
Redistribution of Class 2 Supervisors
Class 3
Class 4
Class 5
Redistribution of Class 6 Supervisors
Class 7
Class 8
Class 9
ESeC Distributions for ESS countries
0%
20%
40%
60%
80%
100%
%
AT BE CH CZ DE DK EE ES FI FR GR HU IC IE IL IT LU NL NO PL PO SI SK SW UA UK
Country
Routine
Lower technical
Lower sales and service
Lower supervisors andtechnicians
Small employers and self-employed -agriculture
Small employers and self-employed -non-agriculture
Intermediate occupations
Lower mgrs &professionals, highersupervisory & technicians
Large employers, highermgrs & professionals
Measuring Employment relations in the ESS
• Looking for core variables over numerous rounds of the surveys:
• Round 1 Citizenship module had two questions now part of core in Round 2 (organisation of work and policy decisions)
• In Round 1 many questions only asked to those who worked in previous week
• In Round 2 also asked for information about last job = larger n
Influence over organisation of daily work
0
1
2
3
4
5
6
7
8
9
10
Largeemployers,
higher mgrs &professionals
Lower mgrs &professionals,
highersupervisory &
technicians
Intermediateoccupations
Smallemployers andself-employed -non-agriculture
Smallemployers andself-employed -
agriculture
Lowersupervisors
andtechnicians
Lower salesand service
Lowertechnical
Routine
Class
Me
an
sc
ore
Two measures of asset specificity
• ‘Using this card, how difficult or easy would it be for you to get a similar or better job with another employer if you wanted to?’
[adapted from Citizenship R1 module]
• ‘In your opinion, how difficult or easy would it be for your employer to replace you if you left?’
[new question]
Two measures of asset specificity
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Largeemployers,
higher mgrs &professionals
Lower mgrs &professionals,
highersupervisory &
technicians
Intermediateoccupations
Lowersupervisors and
technicians
Lower sales andservice
Lower technical Routine
Class
me
an
sc
ore
/1
0
Get new job
Difficult to replace me
Statements about current job (R2)
• Family, work and well-being module• Battery of questions about aspects of job quality:
– variety, on the job learning, security, effort bargain, support from co-workers, time-keeping, health and safety (4 point T/F)
– work effort, work intensity, promotion opportunities (5 point A/D)
• Initial analysis suggests tapping quite different constructs
‘Opportunities for advancement in my job’
0
0.5
1
1.5
2
2.5
3
3.5
Large employers,higher mgrs &professionals
Lower mgrs &professionals,
higher supervisory& technicians
Intermediateoccupations
Lower supervisorsand technicians
Lower sales andservice
Lower technical Routine
Class
Me
an
sc
ore
/5
Subjective General Poor Health
0
0.5
1
1.5
2
2.5
3
Largeemployers,
higher mgrs &professionals
Lower mgrs &professionals,
highersupervisory &technicians
Intermediateoccupations
Smallemployers andself-employed -non-agriculture
Smallemployers andself-employed -
agriculture
Lowersupervisors and
technicians
Lower salesand service
Lower technical Routine
class
mea
n s
core
Looking Within Classes
• ESeC was designed as a ‘nested hierarchy’: each class has a number of distinct groups below the top level.
• Revised ESeC now has 41 active SEGs
• Coding structure offers chance to make fine distinctions among the inactive groups which can be used in modelling
Examples of SEGs
Class 1:
11. Employers (non-agric) with 10+ employees12. Large business farmers13. Higher managerial and administrative14. Higher professional occupations (employees)15. Higher professional occupations (self-
employed)
Examples of SEGs
Class 2:
21. Lower managerial and administrative occupations
22. Lower professional occupations (employees)
23. Lower professional occupations (Self-employed)
24. Higher technician occupations (employees)
25. Higher technician occupations (self-employed)
26. Higher supervisory occupations
Employment Relations through Work Autonomy (difficulty of monitoring)
The ESS invited respondents to say• ‘how much the management at your work allows
you….• to be flexible in your working hours?• To decide how your own daily work is
organised?• To influence your environment?• To influence decisions about the general
direction of your work?• To change your work tasks if you wish to?
Five-item work autonomy scale:Employees in Class 1 and 2
0
1
2
3
4
5
6
7
8
Higher man & admin Higher prof emps Low er man &admin Low er prof emps Higher tech emps Higher supers
Influence on organising own work: SEGS in class 1 and 2
0
1
2
3
4
5
6
7
8
9
10
Largeemployers
Higher man& admin
Higher profemps
Higher profs/e
Low er man&admin
Low er profemps
Low er profs/e
Higher techemps
Higher techs/e
Highersupers
class
mean
sco
re
Subjective Poor Health: Classes 1 and 2
1.85
1.9
1.95
2
2.05
2.1
2.15
2.2
2.25
2.3
Largeemployers
Higher man& admin
Higher profemps
Higher profs/e
Low er man&admin
Low er profemps
Low er profs/e
Higher techemps
Higher techs/e
Highersupers
SEG
mea
n s
core
Conclusions
• ESeC classes discriminate remarkably well:– a range of ‘employment relations’ questions– significant differences between every class, not just
contract types
• Little or no discernable loss of power in adopting an ESeC based on three digit ISCO
• SEGs offer chance to discriminate and structure within classes, but more reliant on precise ISCO