Socio-economic Classifications derived from ISCO08 (and 88)

1. Socio-economic Classifications derived from ISCO08 (and 88) Eric Harrison, City University London InGRID Expert Workshop Amsterdam, Feb 10-12, 2014

2. ‘If you want to get rid of fuzzy job titles, then the last thing you should do is ask people for their job titles’. (Birch 2014) For data analysts, titles are just portals to tasks

3. Overview • Preliminaries: Occupation and Class • The ‘ESeC’ • Validation with EWCS and ESS • Conclusions

4. From Occupation to Social Class • The accurate measurement of occupation is valuable not only in its own right, but also as a building block needed to derive many widely-used class schemas. • In the context of cross-national research this also requires a high degree of harmonisation across countries. • Despite considerable efforts in this direction, users accept that a combination of institutional differences and human error lead to less than optimal validity. • Thus however conceptually rigorous the class measure, it is likely to be operationalised using survey data that is frequently imperfect and always incomplete.

5. Occupation-based class schemas: Briefly • Employers, self-employed, employees • Then within employees….different types of employment • Lockwood – Market situation v Work situation • Goldthorpe – redevelops and formalises this in terms of ‘contractual hazards for employers’ • Asset specificity (marketable skills) • Monitoring problems (autonomous work situation)

6. Different forms of employer response Service relationship Labour contract Pure and modified forms of both (lower managers & professionals, skilled workers) ‘Mixed’ forms of employment regulation where one dimension is high and one low

7. Why surveys need social class variables • Sociology’s ‘only independent variable’ • It matters theoretically: central to both Marxian and Weberian discussions of inequality of resources and life-chances • It works empirically: in many different versions and schemes it predicts y and explains r2 • Most of the attitudes and/or behaviours measured by surveys will be correlated with class – users will expect to be able to do analysis

8. Why class analysts need social surveys • Class positions are not inherently known to respondents – they are rooted in a person’s employment relations • Can’t measure the subtleties of a person’s individual work and market situation (latent variables) • We use a series of proxy questions to establish the employment relations that are typical of what we do know about that person’s employment relations

9. Deriving class from survey information • Current job (in work), last job (unemployed), career typical job (retired) (1) • Employment status (emp/self-emp) (1) • If self-emp how many workers, if emp how many co-workers (2) • Supervisory responsibility? If so, how many for? (2) • Industrial sector of job (1) • Occupation (3)

10. And that’s just respondents! • Not all are economically active: if not, need to ask all those questions about partner (8) • If no partner in place and/or if respondent living at home , may revert to status of principal parent • May be interested in father and mother’s class position anyway (social origins and destinations) so ask questions about them too

11. Occupation is Key • Though it’s nice to have full information, occupation drives 85%+ of a class allocation. • Employment relations emerge out of occupation-specific custom and practice • This makes its measurement crucial • Accuracy of posting to main group • Precision in specifying detailed occupation within this (up to 4 digits)

13. ‘ESeC’

14. ‘ESeC’

15. ‘ESeC’

16. ISCO and ESeC High degree of equivalence: Isco groups 1 and 2 map to top classes, and group 9 to class 9 3000 – 7000 much fuzzier. Associate professionals, technical and service work So need more than 1 or 2 digits

17. Rebasing ESeC on ISCO08

18. Three forms of validation • Operational – does it work, can we apply it to a range of datasets? What happens after loss of information? • Criterion – does it measure what it purports to measure? • Construct - Does it predict the sorts of outcomes that theory suggests it should?

19. Data from the EWCS • Fieldwork Jan – June 2010 • Face to face interviews outside the workplace • 43,816 respondents 15+ in employment • 34 countries • Average RR =44% (31to 74%) • Most detailed survey of employment conditions and experiences • Long running series – EWCS #1 in 1991

20. If you compare your situation with Jan 2009... • ...have you experienced a change in the number of hours you work per week? • ...have you experienced a change in your salary or income? • Responses: Decrease, No change, Increase

21. Change in Working time since 2009

22. Change in earnings since 2009

23. Generally, does your main paid job involve... • ...meeting precise quality standards? • ...assessing yourself the quality of your work? • ...solving unforeseen problems on your own? • ...monotonous tasks? • ...complex tasks? • ...learning new things? • Dichotomous response

24. Job Content & Quality

25. Is the pace of your work dependent on... • ...the work of colleagues? • ...demands of customers, passengers, clients etc? • ...production or performance targets? • ...movement of a machine or product? • ...the direct control of your boss? • Dichotomous response

26. Pace of work determined by...

27. Are you able to choose or change... • ...your order of tasks? • ...your methods of work? • Your speed or rate of work? • Dichotomous response

28. Autonomy over aspects of job

29. Discussion • Broad ‘tip to toe’ fit with expected class ‘gradient’ • Distinctive self-employment spikes consistent with theory and experience • Anomalies in classes 3, 6 and 7 largely consistent with theory: Classes are relational not hierarchical • Some interesting results on individual measures • Customer /market ethos pervades modern societies • Total quality improvement and target setting do not respect employment relations boundaries

30. Three forms of validation • Operational – does it work, can we apply it to a range of datasets? What happens after loss of information? • Criterion – does it measure what it purports to measure? • Construct - Does it predict the sorts of outcomes that theory suggests it should?

31. ESS R5 data on work and wellbeing in recession • ESS rotating module geared specifically to concepts of interest • Asset specificity (how easy for you to get another job + how difficult for employer to replace you?) • Monitoring problems (how difficult for your immediate boss to know how much effort you’re putting in?) • Job quality (variety in work + learn new things + support from co-workers + security)

32. Analysis Strategy • Pooled R5 dataset with controls for country • Dummies for each class (reference class 9) • Controls for age, age sq, education (high, medium, ref= low), gender. • Separate models for each of four derivation methods: • 3 digit occupation + supervision • 2 digit occupation + supervision • 3 digit occupation minus supervision • 2 digit occupation minus supervision

33. Asset Specificity (0-20 scale) OLS coefficients (ref = class 9)

34. Monitoring Problems • Few if any significant results...

35. Job Quality (0-12 scale) OLS coefficients (ref = class 9)

36. Subjective Health (1-5 scale)

37. Happiness (0-10 scale)

38. Discussion • Classes 3 and 6 sometimes in ‘wrong order’ but significantly distinct from each other – ‘mixed classes‘ • Class 8 outperforms 7 on criterion measures but this is reversed for construct validity • Incomplete information has minimal impact on utility of scheme – but class 6 remains a problem • Country by country analysis and experiments with different and fewer classes needed

39. ESRA, Lausanne, 2011 Challenges to measurement of class through occupation • Working with international standard instrument - the new ISCO revision will take time to work through • Improving measurement in existing modes and adapting to changes in mode (self-administered web surveys) • Enforcing better harmonisation across countries in both their collection of national data and their mapping to international measures

40. Remaining Issues Sticking to 2 (or even 1!) digit ISCO would save time, reduce resource burden, increase national comparability But over time would undermine theoretical foundations, i.e. individual occupations are where ER are embedded Could use collapsed class schemas for general surveys but devote more resources to specialist research into social stratification and mobility across Europe (and beyond…)

41. Thank you for listening. Correspondence: eric.harrison.2@city.ac.uk