ESRC - NCRM - Apr 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’

ESRC - NCRM - Apr Part 1: Data on occupations In the social sciences, occupation is seen as one of the most important things to know about a person  Direct indicator of economic circumstances  Proxy Indicator of ‘social class’ or ‘stratification’ GEODE and DAMES –how social scientists use data on occupations – /

ESRC - NCRM - Apr 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 –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 descriptionEmployment statusSOC-2000EMPST Miner (coal)Employee81227 Police officer (Serg.)Supervisor33126 Electrical engineerEmployee21237 Retail dealer (cars)Self-employed w/e12342 Example 2: European Social Survey, parent’s data Occ descriptionSOC-2000EMPST Miner?8122?6/7 Police officer?3312?6/7 Engineer?? Self employed businessman???1/2

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ESRC - NCRM - Apr 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) idougsex.ougCS-MCS-FEGPidougCS I II VIIa

ESRC - NCRM - Apr Occupational information resources: small electronic files about OUGs… Index units# distinct files (average size kb) Updates? CAMSIS, Local OUG*(e.s.) 200 (100)y CAMSIS value labels Local OUG50 (50)n ISEI tools, home.fsw.vu.nl/~ganzeboom Int. OUG20 (50)y E-Sec matrices Int. OUG*(e.s.) 20 (200)n Hakim gender seg codes (Hakim 1998) Local OUG2 (paper)n

ESRC - NCRM - Apr For example: ISCO-88 Skill levels classification

ESRC - NCRM - Apr and: UK 1980 CAMSIS scales and CAMCOM classes

ESRC - NCRM - Apr 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

ESRC - NCRM - Apr 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 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 Micro-data Britain  BHPS 1991, 4537 adults yrs in work  2710 adults observed every year till 2002 Sweden LNU 1991, 2538 adults yrs in work Linked to PRESO administrative data until 2002 [Tomas Korpi] Unemployment (m/f; employees)BrSw Ever Unemployed % / 23%36% / 39% Unemployed for >1 year % / 6%26% / 29% ‘Incidence rate’ (time Un. / active time)3.4 / 2.3 Cumulative rate (log of total time Un.)1.5 / / 2.3

ESRC - NCRM - Apr => 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

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ESRC - NCRM - Apr 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

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ESRC - NCRM - Apr 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

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ESRC - NCRM - Apr 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 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 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 ). 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, 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), 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 ). 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), 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 Paper presented to the Workshop on the Application of ESeC within the European Union and Candidate Countries, June 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)

ESRC - NCRM - Apr Appendices

ESRC - NCRM - Apr Picture – uploading data file

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ESRC - NCRM - Apr Searching – uncurated resources

ESRC - NCRM - Apr Searching – curated resources

ESRC - NCRM - Apr Java portal picture