Putting Europeans into Classes: Operationalising the new European Socio-economic Classification

1. Putting Europeans into Classes: Operationalising the new European Socio-economic Classification Presentation to Eurostat’s Task Force on Harmonised Variables Luxembourg 20 April 2006 David Rose & Eric Harrison ISER University of Essex

2. Eurostat Statistical Harmonisation Programme Aims to create a common set of core units core variables and core classifications for use in European and national social statistics

3. The ESeC project • Expert Group appointed by Eurostat in January 2000 • Proposals for an ESeC made in 2001 Feasibility Report • This report forms the basis for the project

4. ESeC Consortium • Led by ONS • Teams from Universities of Warwick, Essex, Erasmus Rotterdam, Mannheim, Stockholm, Milan, ESRI (Dublin) and INSEE • In contact with all EU NSIs and Eurostat

5. What is an SEC? A generic term for any measure designed to show how societies are stratified Occupational division of labour forms basis of all SECs SECs may be continuous or categorical measures: ESeC is categorical

6. Objectives of SECs Two purposes: • Discrimination • Structuring

7. Discrimination Grouping individuals with similar socio-economic characterisitics into categories Purpose: to analyse a variety of social domains, e.g. health inequalities, educational inequalities, social mobility, income differentials

8. Structuring Structuring of social statistics, in order to: • provide a common language for research across the social and policy sciences • inform the public and policy makers.

9. The Need for ESeC • Ongoing enlargement of European Union • Much ‘comparative’ research just uses national statistics from different countries • Wouldn’t do prices or earnings work in different currencies – Crime? Health? • Need common units and variables

10. Basic SEC Positions EMPLOYERS SELF-EMPLOYED WORKERS EMPLOYEES EXCLUDED The Derivation of the ESeC

11. Distinguishing among Employees • Between 70 and 90 per cent of workforce across Europe fall into ‘employee’ category • Need for much greater differentiation, so create framework based on employment relations

12. Conceptual roots • John Goldthorpe’s work provides such a framework • Difference in type of contractual arrangements • Labour contract: • Short-term effort bargain • Payment by time or piece • Service relationship: • More diffuse exchange of salary for service • Greater security of tenure • Mixed

13. Why this variation in contracts? • Reflection of problems for employers • Combination of • How difficult employees are to replace (‘asset specificity’) • How difficult it is to oversee and control the work of employees (‘monitoring problems’) • These two dimensions, AS and MP, are the underlying concepts which may explain the pattern of employment relations

14. Dimensions of work as sources of contractual hazard, forms of contract and class locations Specificity of human assets high 1 6 Service relationship mixed Difficulty of monitoring low high mixed Labour contract 3 low 9

15. What does the ESeC NOT measure? • Skill • Education • Income Why not? All these things are important so need to be separate for analytical purposes ‘One concept, one measure’

16. Conceptual Derivation of ESeC

17. Form of Classification The outline classification takes the form of a two-level nested hierarchy, similar to classifications such as the French PCS. In its disaggregated form (level 2, what we term ‘Socio-economic Groups’ - SEGs) it covers the whole population at the individual level. There are SEGs to cover various ‘other active’ and inactive groups. Individuals in these groups on the basis of current status may then be re-allocated to ESeC classes in a variety of ways, depending partly on aims of analyst.

18. Prototype ESeC ‘Classes’ (Level 1) • Large employers, higher managerial and professional occupations • Lower managerial and professional occupations • Intermediate occupations • Small employers and own account workers • Employers and self-employed in agriculture • Lower supervisory and lower technician occupations • Lower services & sales occupations • Lower technical occupations • Routine occupations • Never worked and long term unemployed

19. Underlying ESeC ‘Socio-economic Groups’ (Level 2) 11. Employers (other than in agriculture) with 10+ employees 12. Farmers with 10+ full-time employees (or ‘large business’ farmers) 13. Higher managerial occupations 14. Higher professional occupations (employees) 15. Self-employed professional occupations Class 1 Large employers, higher managerial and professional occupations

20. Flexibility One of the advantages of a nested two-level schema such as this is that it will permit analysts to look ‘inside’ classes. This will assist them in understanding how life-chances may vary between groups with the same employment relations.

21. Other active groups 01. Other unemployed 02. Unpaid family workers 03. National service Inactive groups 04. Retired 05. Students (full-time) 06. Children 07. Permanently sick and disabled 08. Looking after home Not classifiable 00. Not classifiable (occupations not given or inadequately described etc.)

22. Classification rules for the individual level of ESeC The ‘other unemployed’ in SEG 01, unpaid family workers in SEG 02, national service personnel in SEG 03 and the inactive SEGs 04 - 08 do not immediately collapse to any class. Rather, individuals in these groups are (re-) allocated to the group of their ‘career typical’ (usually last ‘main’) job or to their household class.

23. Household level rules The household level of this classification would work in a similar way, except that the ESeC class position (level 1) would be allocated through a household class measure. In this case, those in SEGs 01-08 and 00 would be allocated to their household class. Equally, those allocated to SEGs in classes 1 to 9 would take on the ESeC values of their household.

24. Number of SEGs However, it is the classes themselves that are being validated. The question of which SEGs ‘exist’, therefore, relates to which useful class sub-divisions we might wish to make among those combinations of occupation and employment status that share similar employment relations.

25. Constructing ESeC For a fully operationalised ESeC we need measures of: • occupation • status in employment • enterprise size • labour market position

26. Occupation ISCO88(COM) is a core variable for the Eurostat harmonisation programme and so is the obvious measure of occupation to use for ESeC ISCO information only usually released at 2 or 3 digit level So ESeC must be applicable at 3 and 2 digits

27. Status in employment All SECs distinguish between employers, the self-employed (own account workers) and employees. In the EU context, we may need to add the category of unpaid family worker. The EU harmonised variable is ICSE-93.

28. Number of employees It is necessary to distinguish between ‘large’ and ‘small’ employers. Since ISCO88(COM) is the harmonised occupational classification, then the initial simple rule for ESeC is that adopted by ISCO for employers: 1-9 and 10+.

29. Labour market position It is necessary to distinguish more than activity status. Our theoretical model requires us to discriminate between employers by size and between supervisors and other employees. Managerial status is solely dependent on allocation to Major Group 1 of ISCO88(COM). Thus, labour market position involves a combination of ICSE-93, enterprise size and supervisory status.

30. Extract from Prototype ESeC matrix

31. Problems • How to define and distinguish • Managers • Supervisors • ‘Skilled’ workers

32. Managers Who are the real managers? How do we know? • lower ER scores in the UK than we would expect for Class 1 for some OUGs in minor groups 121-3 • much lower ER scores than we would expect for Class 2 for most OUGs in minor group 131 • Measurement error in coding to ISCO groups 12 and 13

33. Supervisors Who are the real supervisors? How do we know? EULFS asks whether people have supervisory responsibilites We need to know if supervision is the main task in someone’s job

34. ‘Skilled’ or lower technical occupations Who are the real skilled workers? How do we know? According to UK ER data, rather few skilled OUGs have ER scores that merit them being placed in the ‘skilled’ working class 8 Research using ESS data suggests large degree of variability in ERs between countries

35. Reduced ESeC Data sets often do not contain full information, so we can produce a ‘reduced ESeC’, derived in essentially the same way as the full form of ESeC, except that the employment status variable would have fewer categories If no information on establishment size, ESeC category for self-employed with employees would be based on modal employment status category for each occupation If no information on supervisory status, allocate to the same category as for ‘other employees’.

36. Simplified ESeC The simplified form of ESeC would be for data sets in which only information on occupation (i.e. ISCO) is available. Convention is to allocate occupations to the modal ESeC category – usually that for ‘employees’, except in occupational groups dominated by self-employment.

37. Varieties of ESeC Thus we can in principle construct matrices for every combination of occupation and employment status e.g. possible to allocate two-digit ‘sub-major groups’ to ESeC classes based on the modal class within constituent minor groups

38. Prototype ESeC • The prototype ESeC we have developed is a common one for all EU states, based on 3-digit ISCO • This is the version currently being examined by NSIs

39. Future Possibilities • Develop ESeC for national occupational classifications. The French team is attempting this with PCS • Develop 4-digit ISCO-based ESeC with national variations in the allocation of occupations to classes • Both ideally require EU-wide research on employment relations

40. Criterion validation On the basis of a common ESeC matrix, consortium members have been examining how effective ESeC is as a measure of employment relations (ER) However, truly effective measures of ER are difficult to obtain from current survey data Ideally need EU-wide survey of ER for more thorough validation

41. Construct Validation Consortium members also examining how effective ESeC is as a descriptive and explanatory variable, i.e. how well ESeC discriminates and structures

42. Source: Schizzerotto et al, Italian Validation Study

43. Persistent Poverty & DeprivationAll Countries (source: Watson et al, Irish validation study)

44. Cross-classification of Persistent Poverty & DeprivationAll Countries (Watson et al)

45. Proportion of respondents with ‘poor’ health according to ESeC class. Men.Source: Kunst et al, Dutch validation study

46. Prevalence of “poor” health by ESEC Class. Northern compared to southern countries. Men.Source: Kunst et al

47. So what will ESeC offer? • A socio-economic classification that is: • Robust (criterion and construct validated) • Flexible (nested structure allows analysis of SEGs within classes) • Comparable (based on harmonised units) • Fully documented (process of creation and validation in public domain) • User-friendly (all derivation matrices and syntax supplied)

48. Summary of Project Milestones • Design and creation of possible models for ESeC (Autumn 2004) • Consultation on models among consortium partners, NSIs, Eurostat and independent experts ► test version (March 2005) • Statistical compendium using European datasets • Validation studies using test version in national contexts (UK, Germany, Sweden, Italy, France) • Validation conference discusses results and produces agreed prototype ESeC (January 2006)

49. Forthcoming Work • Revised validation studies using common prototype ESeC • Trialling of prototype by NSIs on national datasets • Draft User Guide currently in circulation • NSIs’ Workshop – Summer 2006 • Publications and an ongoing ESeC website

50. ESeC is an ‘open project’ and we welcome feedback and involvement from existing and potential users of socio-economic classifications Matrices and syntax available: www.iser.essex.ac.uk/esec