esrc - ncrm - apr 20081 concepts and measures in occupation-based social classifications...
Post on 26-Dec-2015
215 Views
Preview:
TRANSCRIPT
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
ESRC - NCRM - Apr 2008 3
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)
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
ESRC - NCRM - Apr 2008 6
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 .
ESRC - NCRM - Apr 2008 7
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
ESRC - NCRM - Apr 2008 10
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
ESRC - NCRM - Apr 2008 11
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]
ESRC - NCRM - Apr 2008 12
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
ESRC - NCRM - Apr 2008 13
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
ESRC - NCRM - Apr 2008 14
=> 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
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]
ESRC - NCRM - Apr 2008 24
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.
top related