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, 2) Cardiff University,

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

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 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 collections Pre-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 ESSLISESSLIS ISSPCHERISSPCHER Austria  Poland  Belgium  Portugal  Britain  Russia  Czech Rep  Slovakia  Denmark  Slovenia  Germany  Sweden  Hungary  Switz  Ireland 

10 Practicalities: Operationalisations ESSISSPLISCHER 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 schemes Typical associations (eg pooled ESS 2002): ESS country with association extreme higher than average ESS country with association is extreme lower than average CAMSISISEIEGPSkill4 CAMSIS 0.78 (R)0.74 (Eta)0.78 (Eta) ISEI Switz; Irel0.82 (Eta)0.86 (Eta) EGP Pol; Por; Hu Switz Pol Switz 0.56 (CV) Skill4 Slov; Czech 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 stronger Country 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

19 Selected other factors Social mobility Endogamy Income Lifestyles and consumption

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-C0 / 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 scores  CAMSIS v’s ISEI  CAMSIS v’s EGP  CAMSIS 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

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25 CAMSIS v’s ISEI by country ISCO 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

27 CAMSIS v’s EGP by country

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