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Developing a European Socio-economic Classification: Why, What and How iser.essex.ac.uk/esec

Developing a European Socio-economic Classification: Why, What and How www.iser.essex.ac.uk/esec. David Rose & Eric Harrison Institute for Social and Economic Research University of Essex. Questions. Why do we want or need an ESeC? What type of classification is it and what

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Developing a European Socio-economic Classification: Why, What and How iser.essex.ac.uk/esec

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  1. Developing a European Socio-economic Classification:Why, What and How www.iser.essex.ac.uk/esec David Rose & Eric Harrison Institute for Social and Economic Research University of Essex

  2. Questions • Why do we want or need an ESeC? • What type of classification is it and what does it look like? • How is it made and added to datasets?

  3. Why do we want an ESeC? • Eurostat Statistical Harmonisation programme • Much ‘comparative’ research just uses national statistics from different countries • Need to create common set of core: • units • variables and • classifications

  4. ESeC • Expert Group appointed by Eurostat in January 2000 • Proposals for an ESeC made in 2001 Feasibility Report (available from www.iser.essex.ac.uk/esec) • This report forms the basis for the project

  5. Form of classification Outline classification is two-level ‘nested hierarchy’ (see French PCS) Level 1 – nine (ten) classes, reduces to five or three Level 2 – thirty-five (forty-four) socio-economic groups (SEGs) This covers the whole population at the individual level. Includes all the various ‘other active’ and ‘inactive’ groups.

  6. What does the ESeC NOT measure? • Skill • Education • Status or Prestige • Job Complexity

  7. What does the ESeC measure? • Rooted in long traditions within the study of social stratification in European sociology • Employment relations and conditions are central to delineating the structure of socio-economic positions in modern societies

  8. Basic SEC Positions EMPLOYERS SELF-EMPLOYED WORKERS EMPLOYEES EXCLUDED Classifying the European Labour Force

  9. Distinguishing among Employees • Over 80 per cent of workforce ‘employees’! • Differentiate them in terms of employment relations: • Do they have a ‘labour contract’ or a ‘service relationship’? Or a mixture of the two?

  10. Typical elements of the Labour Contract • Short-term exchange of money for effort • Payment by the time or piece • No occupational pension or health scheme • Contract easily terminated • Low level of job security

  11. Typical elements of the Service Relationship • Long-term exchange of service for compensation • Greater job security and employability • Salary • Incremental or similar payment systems • Occupational pension and health schemes • Greater control over the job and thus trust between employer and employee

  12. Possible 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 sales and lower services occupations • Lower technical occupations • Routine occupations • Never worked and long term unemployed

  13. Underlying ESeC ‘Socio-economic Groups’ (Level 2) 11. Employers (other than in agriculture) with 10+ employees 12. Farmers with 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

  14. 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. For example, do higher professionals in SEGs 14 and 15 have better health outcomes when compared with higher managers in SEG 13?

  15. Extra Socio-economic Groups 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 Occupation not given or inadequately described

  16. What to do with the leftovers? • Those in SEGs 01-08 do not automatically collapse to any class. Individuals in these groups are re-allocated to either: • Their ‘career typical’ (usually last ‘main’) job or • their household class.

  17. Household Level Rules • Also possible to re-allocate all SEGs to create a Household version of ESeC • Achieved through the concept of ‘household reference person (HRP) • Usually a given, i.e. part of survey design • But if occupational data on all HH members is available, use ‘dominance’ rules

  18. Constructing ESeC In order for an ESeC to be fully operationalized in line with our theoretical model, at a minimum we require measures of: occupation, status in employment, labour market position and (in some cases) enterprise size. In many countries a measure of farm size may also be necessary

  19. Occupation Measured by ISCO88 (COM) at (up to) 4 digits or a national occupational classification similar to it. Exception is France, but has a Table des Correspondances between the Catégories Socioprofessionnelles (CSP) and ISCO88(COM). ISCO88(COM) is a core variable for the Eurostat harmonisation programme and so is the obvious measure of occupation to use for ESeC.

  20. Some weaknesses in ISCO (1) • Minor group 522 (5.6% of all cases, 8.8% of females in EULFS): • 522 Shop, stall and market salespersons and demonstrators • 5220 Shop, stall and market salespersons and demonstrators • Minor group 419 (3.1% of all cases, 5.1% of females): • 419 Other office clerks • 4190 Other office clerks

  21. Some weaknesses with ISCO (2) • Minor group 827 (0.4% of all cases in EULFS, 0.5% of males) • 827 Food and related products machine operators • 8271 Meat and fish processing machine operators • 8272 Dairy products machine operators • 8273 Grain and spice milling machine operators • 8274 Baked goods, cereal and chocolate products machine operators • 8275 Fruit, vegetable and nut processing machine operators • 8276 Sugar production machine operators • 8277 Tea, coffee and cocoa processing machine operators • 8278 Brewers, wine and other beverage machine operators • 8279 Tobacco production machine operators

  22. What do we do with all the information? • Construct a ‘matrix’ or ‘lookup table’ • The rows are ISCO occupational unit groups • The columns are combinations of employment status and position • E.g. 7323 ‘Glass engravers and etchers’ • Self-employed = class 4 • Supervisors = class 6 • Employees = class 8

  23. ESeC in a world of incomplete information • Some data sets may not contain all the elements required to create ESeC in the prescribed manner. • ECHP: (2 digits ISCO or less – anonymity) • ESS: • French occupations 2 digits • Norwegian self-employed no occupation code • EULFS: Until recently no question about supervisory responsibility

  24. ESeC can cope! Different versions: • ‘Full’: 4 digit ISCO, employment status, management/supervision, establishment size • ‘Reduced’: no data on establishment size • ‘Simplified’: working with occupation via ISCO alone. Sends occupation to most common class for that unit

  25. Using Fewer ISCO Digits • In addition occupation cannot always be coded to four digits – often three or two used with interviewers adding ‘zero’ • The ESeC ‘lookup table’ gives a class value for every possible code in the ISCO book, e.g. • 2000, 2100, 2140, 2142

  26. Timetable of Work • Draft matrices sent out to partners, NSIs, Eurostat and experts for responses - done • Statistical Compendium – completed, appearing soon on website • Validation studies – interim reports delivered, final drafts by Dec 15 • Validation conference in Lisbon – January 19/20, 2006 • ESeC User Guide – Spring 2006 • ESeC Showcase event in Bled – Summer 2006

  27. Request for Assistance/Participation • We want feedback from existing and potential users of socio-economic classifications • Matrices and syntax available: • Contact rosed@essex.ac.uk or ekharr@essex.ac.uk.

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