Stratification - Stirling 20071 Concepts and Measures: Empirical Evidence on the interpretation of...

Stratification - Stirling 2007 1

Concepts and Measures: Empirical Evidence on the interpretation

of ESeC and other occupation-based social classifications

Paul Lambert University of StirlingErik Bihagen University of Stockholm

Paper presented to Social Stratification Research Seminar, Stirling 5-7 September 2007


Summary: occupation-based social classifications

Relevance of reviewing lots of schemes (1) Broad concordance of most measures (2) Optimum measures are ambiguous

(1) Lots of overlap in conceptual correlates (3) A small residual difference does reflect concepts

Sensible taxonomies can rarely be judged true or false, only more or less useful for a given purpose [Mills & Evans, 2003:80]

[EGP]...has a clear theoretical basis, therefore differences between groups in health outcomes can be attributed to the specific

employment relations that characterise each group [Shaw et al., 2007:78]


This review• Relationships between concepts and measures• Properties of various contemporary occupation-based social

classifications– via SOC90 / NYK/ ISCO88 and employment status

• ESeC [Rose and Harrison 2007]

– European Socio-Economic Classification High degree of replicability Empirical validation / criterion validity Standardisation / consistency / widespread use Theoretical integration (with EGP) Compare with unemployment [Elias & McKnight 2003; Chan &

Goldthorpe 2007; Schizzerotto et al 2007]


• Class 1: Large employers, higher grade professional, administrative and managerial occupations: 'the higher salariat'

• Class 2: Lower grade professional, administrative and managerial occupations: higher grade technician and supervisory occupations: 'the lower salariat'

• Class 3: Intermediate occupations: 'higher grade white collar workers'

• Classes 4 and 5: Small employers and self-employed in non-professional occupations: 'petit-bourgeoisie or independents'

• Class 6: Lower supervisory and lower technician occupations: 'higher grade blue collar workers'

• Class 7: Lower services, sales and clerical occupations: 'lower grade white collar workers'

• Class 8: Lower technical occupations: 'skilled workers' • Class 9: Routine occupations: 'semi- and unskilled workers' • Class 10: Never worked and long-term unemployed: 'unemployed' • The non-employed • Six, five and three class models

http://www.iser.essex.ac.uk/esec/guide/class1.php

http://www.iser.essex.ac.uk/esec/guide/class2.php


Micro-data• Britain 1991-2002 BHPS 1991, 4537 adults 23-

55yrs in work 2710 adults observed every

year till 2002

• Sweden 1991-2002• LNU 1991, 2538 adults 23-

55yrs in work• Linked to PRESO

administrative data until 2002 [Tomas Korpi]

Unemployment 1991-2002 (m/f; employees) Br Sw

Ever Unemployed 1991-2002 28% / 23% 36% / 39%

Unemployed for >1 year 1991-2002 9% / 6% 26% / 29%

‘Incidence rate’ (time Un. / active time) 3.4 / 2.3

Cumulative rate (log of total time Un.) 1.5 / 1.2 2.3 / 2.3


Reviewing occupation-based social classifications? GEODE – Grid Enabled Occupational Data Environment, www.geode.stir.ac.uk

[e.g. Lambert et al 2007, International Journal of Digital Curation]

http://www.geode.stir.ac.uk/


=> 31 Occupation-based social classifications

ES5 Employment Status (5) WR Wright (12 categories)

ES2 Employment Status (2) WR9 Wright (9) CM CAMSIS (male scale)

E9 ESeC (9 categories) G11 EGP (11 categories) CF CAMSIS (female scale)

E6 ESeC (6 categories) G7 EGP (7 categories) CM2 CAMSIS (male scale, S)

E5 ESeC (5 categories) G5 EGP (5 categories) CF2 CAMSIS (female, S)

E3 ESeC (3 categories) G3 EGP (3 categories) CG Chan-Goldthorpe status

E2 ESeC (2 categories) G2 EGP (2 categories) AWM Wage mobility score

K4 Skill (4 ISCO categories) MN Manual / Non-M (2) WG1 Wage score (S)

O17 Oesch work logic (17) WG2 Wage score (S)

O8 Oesch work logic (8) ISEI (via ISCO88) WG3 Wage score (B)

O4 Oesch work logic (4) SIOPS (via ISCO88) GN Gender segregation index


Results: Concepts and measures

1) Broad concordance of schemes

2) Ambiguity of optimal schemes

3) Some residual differences do reflect conceptual origins


0.1

.2.3

.4.5

.6.7

.8.9

1C

ram

er's

V

ES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

Employemt Status ESeC schemes EGP schemes Skill classification

Wright schemes Oesch schemes Manual / Non-manual

Britain0

.1.2

.3.4

.5.6

.7.8

.91

Cra

mer

's V

ES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

Men Women

Sweden

(2.1) Categorical - Categorical relations, Cramer's V


0.1

.2.3

.4.5

.6.7

.8.9

1A

nova

R

ES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

CAMSIS / CG Scale ISEI SIOPS

AWM Income averages Gender segregation

Britain0

.1.2

.3.4

.5.6

.7.8

.91

Ano

va R

ES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

Men Women

Sweden

(2.3) Categorical-Metric relations, Anova R


Men and Women (categorical social classifications)

0.1

.2.3

.4.5

.6.7

.8.9

1R

or

pseu

do-R

ES5

E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

Promotion / retention Pay - bonus / increments Hours and level of monitoring

Labour contract type Subjective skill requirements

Men and Women (metric social classifications)

0.1

.2.3

.4.5

.6.7

.8.9

1R

or

pseu

do-R

CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN

Britain Sweden

(2.6) Associations - Employment Relations and Conditions



1) Broad concordance of schemes• Measures mostly measure the same thing

Generalised concepts are better

• Criterion validity is asymmetric [cf. Tahlin 2007]




-.01

.01

.03

.05

.07

.09

NullES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17O8

O4MN

CMCF

CGISEI

SIOPAWM

WG3GN

Pseudo R-squared Increase in BIC

Britain, Males

-.06

-.04

-.02

0.0

2.0

4.0

6

NullES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17O8

O4MN

CMCF

CM2CF2

ISEI

SIOPAWM

WG1WG2

GN

Sweden, Males

(3.4a) R-2 and BIC for predicted unemployment risk




2) Ambiguity of optimal schemes Balancing explanatory power and parsimony No schemes stand out as substantially stronger ESeC & EGP 3- and 2-class versions limited AWM favourable in Sweden



0.0

25

.05

E9

E3G11

G7K4

CMISEI

AWM

Decrease in log-like

Increase in BIC

(1): with additional explanatory variables

0.0

25

.05

.075

E9

E3G11

G7K4

CMISEI

AWM

(2): (1) plus industry indicator variables

0.0

25

E9

E3G11

G7K4

CMISEI

AWM

(3): Heckman selection, Industry = public sector services

0.0

25

E9

E3G11

G7K4

CMISEI

AWM

(4): Heckman selection, Industry = private manufacturing

(4.1): Unemployment risks (British men)


EGP cf. CAMSIS – critical individuals

Britain (males)

Better EGP predicted risk of Un. (H – rightly higher; L – rightly lower)

7121 (L) Builders (traditional)

8322 (L) Car / taxi drivers

1314 (L) Wholesale / retail managers

7141 (L) Painters

7231 (H) Motor mechanics

2411 (H) Accountants

4131 (H) Stock clerks

7124 (H) Carpenters / joiners

8324 (H) Truck / Lorry drivers

Better CAMSIS predicted risk of Un. (H – rightly higher; L – rightly lower)

5169 (L) Protective service workers

4212 (L) Tellers / counter clerks

4190 (L) Office clerks

7230 (L) Machinery mechanics/fitters

1314 (H) Wholesale / retail managers





3) Some residual differences do seem to reflect conceptual origins

Differences between schemes diminish but don’t vanish G11 in Br explains more Unemp. [as Chan & Goldthorpe 2007] E9 in Sweden explains more Unemp. ??Are empirical differences due to (the concepts / employment

relations of) certain specific occ.s


Conclusions

• Do measures measure concepts? – Yes (sometimes) – criterion validity

– No (not uniquely)

• How should we choose between measures? – Practical issues

– Conceptual assumptions – generalised schemes

• What about ESeC? – Few clear strengths in empirical properties

– Practical advantages if widely used


References

• Chan, T. W., & Goldthorpe, J. H. (2007). Class and Status: The Conceptual Distinction and its Empirical Relevance. American Sociological Review, 72, 512-532.

• Elias, P., & McKnight, A. (2003). Earnings, Unemployment and the NS-SEC. In D. Rose & D. J. Pevalin (Eds.), A Researcher's Guide to the National Statistics Socio-Economic Classification. London: Sage.

• Goldthorpe, J. H., & McKnight, A. (2006). The Economic Basis of Social Class. In S. L. Morgan, D. B. Grusky & G. S. Fields (Eds.), Mobility and Inequality. Stanford: Stanford University Press.

• Lambert, P. S., Tan, K. L. L., Turner, K. J., Gayle, V., Prandy, K., & Sinnott, R. O. (2007). Data Curation Standards and Social Science Occupational Information Resources. International Journal of Digital Curation, 2(1), 73-91.

• Mills, C., & Evans, G. (2003). Employment Relations, Employment Conditions and the NS-SEC. In D. Rose & D. J. Pevalin (Eds.), A Researchers Guide to the National Statistics Socio-economic Classification (pp. 77-106). London: Sage.

• Rose, D., & Harrison, E. (2007). The European Socio-economic Classification: A New Social Class Scheme for Comparative European Research. European Societies, 9(3), 459-490.

• Schizzerotto, A., Barone, R., & Arosio, L. (2006). Unemployment risks in four European countries: an attempt of testing the construct validity of the ESeC scheme. Bled, Slovenia, and http://www.iser.essex.ac.uk/esec/: Paper presented to the Workshop on the Application of ESeC within the European Union and Candidate Countries, 29-30 June 2006.

• Shaw, M., Galobardes, B., Lawlor, D. A., Lynch, J., Wheeler, B., & Davey Smith, G. (2007). The Handbook of Inequality and Socioeconomic Position: Concepts and Measures. Bristol: Policy Press.

• Tahlin, M. (forthcoming). Class Clues. European Sociological Review.


Appendices


Males, Britain-2-1

01

2

Males, Sweden

-2-1

01

2

Higher salariatLower salariat

IntermediateSmall Self-emp

FarmersLow. sup./technic

Low. serv./cler.Low. technical

Routine

Ever experienced unemployment 1991-2002 Unemployment incidence rate

1 year or more unemployed Log of total unemployment

Source: LNU and BHPS 1991-2002

Comparison intervals for ESeC categories, by unemployment measure

Figure 3.1: Predictions of Unemployment risk, 1991-2002


Background – handling occupational data [e.g. Lambert et al 2007, International Journal of Digital Curation]

Model is: 1) Record and preserve ‘source’ occupational data (i.e OUG)2) Use a transparent translation code to derive occupation-based

social classifications

Challenges include:– Locating occupational information resources

http://home.fsw.vu.nl/~ganzeboom/pisa/http://www.iser.essex.ac.uk/esec/consort/matrices/

– Large volumes of data (country; time; updates) – Detail on occupational index units (OUGs)– Gaps in working practices (software; NSI’s v’s academics)

ESeC has many attractive features: well documented scheme with ‘criterion validity’; transparent access in SPSS; wide adoption likely

http://home.fsw.vu.nl/~ganzeboom/pisa/

http://www.iser.essex.ac.uk/esec/consort/matrices/


Men and Women (categorical social classifications)0

.1.2

.3.4

.5.6

.7R

or

pseu

do-R

ES5

E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17 O8

o4MN

Promotion / retention Pay - bonus / increments Hours and level of monitoring

Labour contract type Subjective skill requirements

Men and Women (metric social classifications)

0.1

.2.3

.4.5

.6.7

R o

r ps

eudo

-R

CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN

Britain Sweden

(2.6b) Employment Relations / Conditions (excluding contract data)


-.05

0.0

5.1.

15.2

.25.

3

ES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17O8

O4MN

CMCF

CGISEI

SIOPAWM

WG3GN

Increase in R-squared Increase in BIC

Britain

-.05

0.0

5.1

.15

.2.2

5.3

.35

ES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

WRWR9

O17O8

O4MN

CMCF

CM2CF2

ISEI

SIOPAWM

WG1WG2

GN

Sweden

(3.5) R-2 and BIC for Years of Education


-.01

.01

.03

.05

.07

.09

NullES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

MN

CMCF

CGISEI

SIOPAWM

WG3GN

Pseudo R-squared Increase in BIC

Britain, Males (Father's effects)

-.01

.01

.03

.05

NullES5

ES2E9

E6E5

E3E2

G11G7

G5G3

G2K4

MN

CMCF

CM2CF2

ISEI

SIOPAWM

WG1WG2

GN

Sweden, Males (Father's effects)

(3.4c) R-2 and BIC unemployment risk, father's scheme


0.0

25.0

5

E9

E3G11

G7K4

CMISEI

AWM

Decrease in log-like

Increase in BIC

(1): with additional explanatory variables

0.0

25.0

5

E9

E3G11

G7K4

CMISEI

AWM

(2): (1) plus industry indicator variables

0.0

25

E9

E3G11

G7K4

CMISEI

AWM

(3): Heckman selection, Industry = public sector services

0.0

25

E9

E3G11

G7K4

CMISEI

AWM

(4): Heckman selection, Industry = private manufacturing

(4.2): Unemployment risks (Swedish men)

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