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Dr Paul Lambert and Dr Vernon Gayle University of Stirling

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

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Dr Paul Lambert and Dr Vernon Gayle University of Stirling

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  1. Concepts and Measures in occupation-based social classificationsPresentation 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 person • Direct indicator of economic circumstances • Proxy 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: • Record and preserve ‘source’ occupational data (i.e OUG) • 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) ESRC - NCRM - Apr 2008

  4. Stage 1 - Collecting Occupational Data

  5. www.geode.stir.ac.uk/ougs.html ESRC - NCRM - Apr 2008

  6. GEODE provides services to help social scientists • Disseminate, and access other, Occupational Information Resources • Link together their (secure) micro-data with OIR’s ESRC - NCRM - Apr 2008

  7. Occupational information resources: small electronic files about OUGs… ESRC - NCRM - Apr 2008

  8. For example: ISCO-88 Skill levels classification ESRC - NCRM - Apr 2008

  9. and: UK 1980 CAMSIS scales and CAMCOM classes 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] Sensible taxonomies can rarely be judged true or false, only more or less useful for a given purpose [Mills & Evans, 2003:80] • 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 [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. 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] Micro-data ESRC - NCRM - Apr 2008

  14. => 31 Occupation-based social classifications ESRC - NCRM - Apr 2008

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  18. What measures measure • 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

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  20. What measures measure • Construct validity is.. • also asymmetric • conflated by level of occupational detail • 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

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  22. EGP cf. CAMSIS – critical individuals ESRC - NCRM - Apr 2008

  23. Measures in multivariate context • 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 ESRC - NCRM - Apr 2008

  25. 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.

  26. Appendices ESRC - NCRM - Apr 2008

  27. Picture – uploading data file ESRC - NCRM - Apr 2008

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  29. ESRC - NCRM - Apr 2008

  30. Searching – uncurated resources ESRC - NCRM - Apr 2008

  31. Searching – curated resources ESRC - NCRM - Apr 2008

  32. Java portal • picture ESRC - NCRM - Apr 2008

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