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1
Occupational Stratification Measures in Harmonised
European Surveys
Talk prepared for ISA RC28 Spring Meeting, Neuchatel, 7-9 May 2004
Paul Lambert
Ken Prandy 1) Stirling University, [email protected]
2) Cardiff University, [email protected]
2
1. Yet more biassed navel gazing?
2. Data: countries, schemes, surveys
3. Four Evaluations:
i) Practical
ii) Theoretical
iii) Empirical
iv) Relativism
4. Conclusions
Assessing occupational schemes:
3
This paper..
• Full version hopefully written June/July• Two previous related works downloadable:
– Prandy, Lambert & Bergman 2002 (relation of schemes to income & education measures for LIS & ISSP)
– Lambert & Prandy 2003 (relations to cultural variables, & impact of life transitions for CHER)
For updates / references / files, contact [email protected]
4
1. Introduction: Why keep on evaluating occupational schemes?
• Previous studies: – Occupational patterns fixed in time & space (eg Treiman `77)
– Properties / benefits of specific schemes (eg Wright `97; Ganz. et al `92,`96; EGP papers)
– Projects developing new schemes: E-SEC
– Investments in schemes: bias or advocacy?
• However…– Data resources (& govt classifications) keep being updated
– Relatively few multiple-scheme reviews
5
..and, trends in cross-national analysis:
• Additions from new countries / economies• Widening time spells increasingly span periods of
economic change• Harmonisation of questionnaires and design
(eg Harkness et al 2003), replacing post-hoc • Disclosure control fears less detail in variables• Speed of access and delivery / wider and non-
specialist user communities
6
2) Data Resources : Occupational classification schemes
3 schemes fixed in time and place: • ISEI : Ganzeboom et al `92 – ‘Status’• EGP : Erikson and Goldthorpe `93, 7
category scheme• ‘Skill4’ : ISCO88 based 4-category
classification of skill levels, from Elias `97– (4 skill levels = major groups {1 &} 2; 3;
4,5,6,7 & 8; and 9).
7
One ‘relativistic’ scheme: CAMSIS
• ‘Cambridge Social Interaction and Stratification Scales’, see www.cf.ac.uk/socsci/CAMSIS/
• Separate derivations for gender groups, countries, and time periods
• ..or at least when they have been calculated..
Measure of occupational stratification reflecting the typical social distances
between occupations, arranged in a single hierarchy representing the dominant
empirical dimension of social interaction
8
Data: 4 cross-national collectionsPre-harmonised: • ESS European Social Survey: cross-sections from
2002 onwards, attitudes and lifestyles, pre-harmonised
Intermediate: post- and pre-harmonisation:• CHER Household Panel Harmonisation: panels
from 1990 onwards, simplified ECHP
• ISSP International Social Science Programme: cross-sections from 1985, attitudes, lifestyles, voting
Post-hoc only: • LIS Luxembourg Income Study (+ LES, LWS):
income and employment harmonisations
9
Data: countries selected by occ info per study
ESS LIS ESS LIS
ISSP CHER ISSP CHER
Austria Poland
Belgium Portugal
Britain Russia
Czech Rep Slovakia
Denmark Slovenia
Germany Sweden
Hungary Switz
Ireland
10
Practicalities: Operationalisations
ESS ISSP LIS CHER
EGP ?
(Some weak empst)
(lacks empst)
(lacks empst & isco)
Skill4 ?
(not all ISCO)
ISEI (except origins)
?
(not all ISCO)
?
(Some weak ISCO)
CAMSIS (except origins)
?
(Some weak ISCO)
11
Practical evaluation: EGP
Translation from ISCO via Ganzeboom ISMF project translations (difficult: requires employment status information, & still ambiguous)
Tension: sparsity of some categories v’s less than 7-category version looses significant info
Considerable variation in distributions by countries and genders
Easily understood and widely publicised Likely to connect with proposed ‘E-SEC’ Some translations possible from other schemes, eg
national SEGs or Occupational groups
12
Practical evaluation: Skill4
ISCO Major group clustering uneven: level 3 is large and heterogeneous
ISCO major groups 1 and 10 are formally excluded (in practice, place in levels 1 & 4)
No easy linkage with non-ISCO data Simple linear translation from ISCO, & only
requires 1-digit of detail Pragmatic gender balance in distributions Options with ordinality Easily understood
13
Practical evaluation: ISEI
No easy linkage with non-ISCO data Not well known in some disciplines / traditions Simple linear translation from ISCO88 (via
Ganzeboom ISMF macros for SPSS, STATA, ..) Documentation and instructions, including major
group average imputations Readily understood / communicated Gender patterns (M > F) make sense to most Treatment as continuous
14
Practical evaluation: CAMSIS Limited wider publicity, & complexity of describing
methods Complex techniques for matching in scores (see
LIS & CHER specific pages, ESS & ISSP to come) Patchy coverage of countries / time periods Fuller implementation requires employment status
information (though can be ignored) Gender treatment counter-intuitive (F > M) Completed versions translate fully with both ISCO
and national specific occupational schemes (downloadable index files)
National specific standardised metric
15
Relations between schemesTypical associations (eg pooled ESS 2002): ESS country with association extreme higher than average
ESS country with association is extreme lower than average
CAMSIS ISEI EGP Skill4
CAMSIS 0.78 (R) 0.74 (Eta) 0.78 (Eta)
ISEI Switz; Irel 0.82 (Eta) 0.86 (Eta)
EGP Pol; Por; Hu
Switz
Pol
Switz
0.56 (CV)
Skill4 Slov; Czech
Irel
Irel Pol; Por
16
3ii) Theoretical evaluation
• Class v’s categories v’s hierarchy– Favour to hierarchy Skill4, ISEI, CAMSIS
• Employment status v occupational position– Use of both EGP, CAMSIS
• Relativism towards countries, genders, time periods– Strongest case for time period, then gender,
then nations, CAMSIS
17
3iii) Empirical Evaluation
Do the patterns of association between schemes and a variety of other measures
differ between schemes, and is this different for different countries, genders, time
periods• Education and other stratification associates• Life transitions• Unit of analysis
18
Education Average correlations from occ measure to education level for
adult populations very stable between schemes and over time, typically ~0.5, ISEI highest. Males usually higher.
Greatest mismatches between schemes include: • Females generally : CAMSIS associations to
education are relatively stronger than others• Females in full time work: CS strongerCountry specific:
– Poland: ISEI much stronger than CS– Switzerland: EGP weaker than all others (1990 & 2001)– Ireland: Male CS weaker than all others – Portugal: All female assocs much higher than male
20
Household structure
CHER 1998: Typical stratification associations for: BW – Both working couple; 1W – One working cple; SW – Single wking
← high to low associations (income; educ; assets) →
Belgium BW,1W, SW
Germany BW,1W, SW
Switzerland BW,1W, SW
UK BW,1W, SW
Denmark BW, 1W, SW
France 1W, BW, SW
Ireland 1W,BW SW
Portugal BW, SW,1W
For most egs, couple type doesn’t alter associations, but single households more distinctive
21
Life Transitions in joint hhld-working situation, associations from CS & educ, income, assets (CHER)
1998 1996
Both work
One works
None work
Single works
Single not wk
BW-C 0 / 0 + / - + / - ++ / ++
OW-C - / - 0 / 0 + / +
NW-C - / - + / + 0 / 0
W-S - / + - / + 0 / 0 ++ / ++
NW-S + / + ++ / ++ - / + 0 / 0
22
3iv) Relativism
CAMSIS scores on same occs in different countries
Male v’s Female CAMSIS scoresCAMSIS v’s ISEICAMSIS v’s EGPCAMSIS v’s Skill4
23
• Patterns: Some plausible differences v’s some probable ‘noise’. Eg structural differences: ISCO major group Professions higher on average in
Germany and Switz for CS than other schemes ISCO major group Crafts higher on average in
Turkey and Germany for CS than for other schemes
German v's Swiss CAMSIS scores, men
Swiss male title-only ISCO 1990
100806040200
Ge
rma
n m
ale
title
-on
ly I
SC
O 1
99
5
100
80
60
40
20
0
24
Belgium Germany Hungary Luxembourg Poland Switzerland
United Kingdom Denmark France Ireland Portugal
Country
Male v's female CAMSIS-CHER scores
ISCO-88 sub-major group scores
Numbers show selected outlying ISCO-88 sub-major group categories.'Smoother line' illustrates aggregate level cross-country male-female links.
25.00 50.00 75.00 100.00
Male CAMSIS scale score by country
25.00
50.00
75.00
100.00
80 8192
11
22
5271
73
91
91
661
22
24
32
52
73
92
22
0
6
22
2432
34
61
25
CAMSIS v’s ISEI by countryISCO major groups and countries with largest
departures, ESS 2002: – Farming generally (CS higher both M & F)– Female clerks (ISEI higher)– Crafts (CS lower for women in most countries)
• Marked variability by majgps: Czech-F; Irel-M; Poland-M/F; Port-F; Swed-F; Slovenia M/F;
• Least variability: Hungary M/F; UK M;
26
CAMSIS v’s Skill4 by country
First skill level Second skill level Third skill level Fourth skill level
skill4
0.00
20.00
40.00
60.00
80.00
100.00
cs
9.00
9.00
1.004.00
3.00
3.003.00
8.00
3.00
3.00
1.001.00
7.00
4.00
3.00
3.003.00
1.001.00
2.002.002.002.002.00
9.00
3.00
3.00
3.00
CountryCzech Republic
United Kingdom
Portugal
Sweden
27
CAMSIS v’s EGP by country
0.00
20.00
40.00
60.00
80.00
100.00
CS 7.00 4.00
9.00
7.00 7.00
5.00 5.00
5.00
9.00
5.00
2.002.002.002.00
7.007.00
7.00
8.00
9.006.00
4.00
Annotation
CountryCzech Republic
United Kingdom
Portugal
Sweden
28
Conclusions
• Basic similarity between schemes – ‘fixed in time and place’ is ok
• Pragmatic differences still significant - ISEI strong, but need for country specific catering
• Theories of cross-national research relativism• Gender differences most important empirical
element of relativism• Several discernible national specific trends :
certain countries (eg E Europe and S Europe) have larger variations