esrc - ncrm - apr 20081 concepts and measures in occupation-based social classifications...

ESRC - NCRM - Apr 2008 1

Concepts and Measures in occupation-based social

classifications

Presentation to: ‘Interpreting results from statistical modelling – a seminar for social scientists’ , Imperial

College, 29th April 2008

Dr Paul Lambert and Dr Vernon Gayle University of Stirling

A seminar for the ESRC National Centre for Research Methods, Lancaster-Warwick Node on ‘Developing Statistical Modelling in the Social Sciences’


Part 1: Data on occupations

• In the social sciences, occupation is seen as one of the most important things to know about a personDirect indicator of economic circumstancesProxy Indicator of ‘social class’ or ‘stratification’

• GEODE and DAMES – how social scientists use data on occupations– www.geode.stir.ac.uk / www.dames.org.uk

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

http://www.dames.org.uk/


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

..Many people recommend this [cf. Bechhofer 1969; Rose and Pevalin 2003] but not all applications do this..

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)

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

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

Stage 1 - Collecting Occupational Data

Example 1: BHPS Occ description Employment status SOC-2000 EMPST

Miner (coal) Employee 8122 7

Police officer (Serg.) Supervisor 3312 6

Electrical engineer Employee 2123 7

Retail dealer (cars) Self-employed w/e 1234 2

Example 2: European Social Survey, parent’s dataOcc description SOC-2000 EMPST

Miner ?8122 ?6/7

Police officer ?3312 ?6/7

Engineer ?? ??

Self employed businessman ?? ?1/2


www.geode.stir.ac.uk/ougs.html


GEODE provides services to help social scientists

1) Disseminate, and access other, Occupational Information Resources

2) Link together their (secure) micro-data with OIR’s

External user

(micro-social data)

Occ info (index file) (aggregate)

User’s output

(micro-social data)

id oug sex . oug CS-M CS-F EGP id oug CS

1 110 1 . 110 60 58 I 1 110 60 .

2 320 1 . 320 69 71 II 2 320 69 .

3 320 2 . 874 39 51 VIIa 3 320 71 .

4 874 1 . 4 874 39 .

5 874 2 . 5 874 51 .


Occupational information resources: small electronic files about OUGs…

Index units # distinct files (average size kb)

Updates?

CAMSIS, www.camsis.stir.ac.uk

Local OUG*(e.s.)

200 (100) y

CAMSIS value labelswww.camsis.stir.ac.uk

Local OUG 50 (50) n

ISEI tools, home.fsw.vu.nl/~ganzeboom

Int. OUG 20 (50) y

E-Sec matrices www.iser.essex.ac.uk/esec

Int. OUG*(e.s.)

20 (200) n

Hakim gender seg codes (Hakim 1998)

Local OUG 2 (paper) n


For example: ISCO-88 Skill levels classification


and: UK 1980 CAMSIS scales and CAMCOM classes


GEODE Occupational Information Depository

• Collects large volumes of OIRs across countries, time periods

• Facilitates communication between producers of occupational information resources

Universality Hitherto the dominant approach same occupation-based measures valid across all

countries/time periods Specificity

different occupation-based measures should be used specific to different countries / time periods

See http://www.geode.stir.ac.uk/publications.html

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


Part 2) Concepts and measures [Lambert and Bihagen 2007]

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]


How to interpret β’s from occupation-based social classifications…

• What the measures measure – Criterion and construct validity

• What measures measure in multivariate context– Approaches to complex analysis


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


=> 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


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


What measures measure

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

Generalised concepts are better Occupation-based measures don’t uniquely measure

the concepts on which they are based (doh!)

• Criterion validity is asymmetric • cf. Tahlin 2007: Skill or employment relations for EGP


-.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


What measures measure

2) Construct validity is.. also asymmetric conflated by level of occupational detail

3) Ambiguity of optimal schemes Balancing explanatory power and parsimony No schemes stand out as substantially stronger Highly collapsed versions are limited

• (e.g. ESeC & EGP 3- and 2-class versions) Metrics are generally fine


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


Measures in multivariate context

4) Multivariate contexts of coefficient effects in occupations…

• ..are generally problematic – ‘everything depends on occupations’• Endogeneity of employment itself• Household / career context of occupations

• Some residual differences do seem to reflect conceptual origins [cf. Chan & Goldthorpe 2007]


Conclusions

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

– No (not uniquely)

• How should we choose between measures? – Practical issues: favour widely used schemes and metrics

– Conceptual assumptions: favour generalised schemes

• What about standardisation (e.g. ESeC)? – Few clear strengths in empirical properties

– Practical advantages if widely used

References• Bechhofer, F. (1969). Occupations. In M. Stacey (Ed.), Comparability in Social Research (pp. 94-122). London:

Heinemann (in association with British Sociological Association / Social Science Research Council).

• 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.

• Hakim, C. (1998). Social Change and Innovation in the Labour Market : Evidence from the Census SARs on Occupational Segregation and Labour Mobility, Part-Time work and Student Jobs, Homework and Self-Employment. Oxford: Oxford University Press.

• Lambert, P. S., & Bihagen, E. (2007). Concepts and Measures: Empirical evidence on the interpretation of ESeC and other occupation-based social classifications. Paper presented at the International Sociological Association, Research Committee 28 on Social Stratification and Mobility, Montreal (14-17 August).

• 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.

• Rose, D., & Pevalin, D. J. (Eds.). (2003). A Researcher's Guide to the National Statistics Socio-economic Classification. London: Sage.

• 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. (2007). Class Clues. European Sociological Review, 23(5)557-572.


Appendices


• Picture – uploading data file


Searching – uncurated resources


Searching – curated resources


Java portal

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esrc - ncrm - apr 20081 concepts and measures in occupation-based social classifications...

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