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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, paul.lambert@stirling.ac.uk

2) Cardiff University, prandyk@cardiff.ac.uk

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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:

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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 paul.lambert@stirling.ac.uk

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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

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..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

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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).

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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

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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

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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

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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)

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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

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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

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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

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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

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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

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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

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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

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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

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Selected other factors

• Social mobility

• Endogamy

• Income

• Lifestyles and consumption

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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

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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

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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

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• 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

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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

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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;

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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

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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

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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