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, 29 th 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’
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ESRC - NCRM - Apr 20081 Concepts and Measures in occupation-based social classifications Presentation to: ‘Interpreting results from statistical modelling.
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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’
ESRC - NCRM - Apr 2008 2
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
– Large volumes of data (country; time; updates) – Detail on occupational index units (OUGs)– Gaps in working practices (software; NSI’s v’s academics)
O4 Oesch work logic (4) SIOPS (via ISCO88) GN Gender segregation index
ESRC - NCRM - Apr 2008 15
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
ESRC - NCRM - Apr 2008 16
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
ESRC - NCRM - Apr 2008 17
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
ESRC - NCRM - Apr 2008 18
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
ESRC - NCRM - Apr 2008 19
-.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
ESRC - NCRM - Apr 2008 20
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
ESRC - NCRM - Apr 2008 21
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)
ESRC - NCRM - Apr 2008 22
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
ESRC - NCRM - Apr 2008 23
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]
• 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.