1. Validation studies : project using French data Assessing the consistency of ESeC with theoretical framework “à la Goldthorpe” Pointing out using ISCO as a common starting point is not necessarily going to be the best way to achieve comparability

2. What data sources can be used to test ESeC? The data : 1998 working conditions survey (supplementing the LFS) allows to code national classification (PCS) and ISCO using PCS and NACE (classification of economic activity ) Complementary data : Adult Education Survey 2000

3. What data can be used to measure status of employment? employee / self-employed / employer

4. What data can be used to measure type of contract (for employees) ? monthly wage; indefinite duration contracts vs. temporary contracts, as well as life employment contracts; working part-time;

5. What data can be used to measure type of contract (for employees) ? tenure whether the person was employed in the same establishment 2 years before, and if so, what was the wage growth over the period

6. What data can be used to measure autonomy/routine? supervisory responsibility, with or without power over the pay and career of subordinates; production-line work; job consists in repeating the same series of operations; pace of work imposed by supervisors or machines or other technical constraints;

7. What data can be used to measure autonomy/routine? person carries out instructions strictly ; instructions specify how to do the work as well as the work to do; person deals with incidents on their own or calls to hierarchy ;

8. What data can be used to measure investment in employee ? whether the person has benefited during the last 12 months from training paid by his/her employer ;

9. What approach to test ESeC? Numerous ER variables selected for their a priori theoretical relevance Define ESeC as group of occupations similar in terms of ER within groups Desirable properties ESeC to be confronted to data

10. What approach to test ESeC? Simple solution : tabulate all ER variables by ESeC category (to be tested) and compare means across groups, in terms of theoretical interpretation Problem : ER is essentially multidimensional, comparing groups in terms of ER variables taken separately is not conclusive

11. What approach to test ESeC? One must construct one or several synthetic measures of ER, based on whole or part of subsets of the ER variables

12. Method used Consider any statistical unit that we want to group into categories as homogeneous as possible in terms of multidimensional ER : for instance ISCO, PCS, CS

13. Method used Since we want to construct ESeC groups as a partition of the above statistical units, decide about the groupings on the basis of a proximity criterion in the space of ER

14. Method used Take the mean of every single ER variable across individuals belonging to each statistical unit The statistical analysis is then carried out on the set of statistical units, each of which is associated with a set of ER variables

15. Method used Question: evaluate the distance between SU in terms of multidimensional ER => CLUSTER ANALYSIS The closer two SU in terms of ER, the higher their proximity in the tree, which is the outcome of the analysis

16. Method used : cluster analysis

17. Results for France Use two different statistical units: => CS because only information available in many if not most French surveys => 2-digit ISCO because it can sometimes be coded using 4-digit PCS combined with NACE Note : ISCO is necessary coded on the basis of PCS which involves coding error

18. Results : Cluster analysis on CS

19. Results : Cluster analysis on 2-digit ISCO

20. What now ? Consider (one or several) exogeneous grouping of SU providing one or several ESeC. Eg. A priori grouping of CS, of ISCO2, grouping of ISCO2 based on UK data and algorithm as V2.1 or V3 Now use data (LFS and WCS98) to code CS and ISCO2 (ie SU) Use groupings to code above ESeC in data (V2.1, V3 or any other definition)

21. What now ? As we are considering the same SU as before, using the same data QUESTION : do we obtain comparable groupings to results of cluster analysis ? This comparison is our test of various ESeC definitions

22. What now ? Most simple criterion :use LFS to cross- tabulate ER groups with ESeCV2, ESeCV3

23. What now ? Alternative approach : compare tree in figure 1 (CS) or figure 3 (ISCO) assume each SU can be interacted with any given definition of ESeC Eg. Consider SU ISCO2=34, according to cross-walk ISCO2 to V2.1 could belong to ESeC groups 2 or 3 (4, 5, 6, 7) according to some additional informations

24. What now ? Come back to individual data, divide group 34 into 342, 343 (345, 346, 347) according to ESeC code. In practice this is the new SU; Average out again ER variables by SU; Do again cluster analysis; Yield tree figure 4 (SU=ISCO2 x ESeC V2.1)

25. What now ? Test the proximity of new SUs; Expect for instance 132 to be closed to 342 Additional empirical test no very conclusive for ESeC

26. Outcome of the statistical analysis Concerning heads of business, the threshold of 10 employees may not be most appropriate : it should be discussed whether 20 or 50 should be used instead; Concerning salespersons, our results suggest that they are closer to routine occupation than to clercks

27. Outcome of the statistical analysis Drivers belong to the group of blue-collar workers according to the French PCS, but also according to our statistical analysis, their position in ESeC should therefore be in group 8 rather than 6 We also mentioned technicians, closer to administrative and service intermediate occupation than to foremen and supervisor