1. Bled Conference on ESeC , 29th-30th June 2006
How to validate protype ESeC ?
An example on French data
Cécile Brousse, INSEE, Employment Unit

2. Plan
1) Where does ESeC come from ? The studies carried out by the consortium (Oct 2004, Jan 2006)
2) How can we test protype ESeC ? Method carried out in the case of France
3) What data and variables are used to test prototype ESeC ?
4) Main results
5) Conclusions : advantages and limits of the method proposed by INSEE ?

3. FIRST PART WHERE DOES ESeC COME FROM
FIRST PART WHERE DOES ESeC COME FROM ? THE STUDIES CARRIED OUT BY THE CONSORTIUM (from october to January 2006)

4. Prototype ESeC back ground : the studies carried out by the consortium (Oct 2004, Jan 2006)
1) « Criterion validation » studies
test the consistency between the conceptual basis (Employment relations) and the prototype ESeC
Four studies have been carried out by researchers (from UK, Sweden, Germany, France)
2) « Construction validation » studies
show if the classification helps understanding social phenomena .
Three studies carried out (on health (the Netherlands, unemployment (Italy) on poverty (Ireland))

5. The validation » studies carried out by the consortium (Oct 2004, Jan 2006)
1) « Criterion validation » studies
test the consistency between the conceptual basis (Employment relations) and the prototype ESeC
Four studies have been carried out by researchers (from UK, Sweden, Germany, France)
2) « Construction validation » studies
show if the classification helps understanding social phenomena .
Three studies carried out (on health (the Netherlands, unemployment (Italy) on poverty (Ireland))

6. « Criterion validation » studies carried out by the consortium (Oct 2004, Jan 2006) (2)
All these studies answer problematics relatively far from each other. In an explicit manner with some (Swedish and Germans) more implicit with others (British and French), the validations consist essentially in comparing ESeC to already existing classifications
ESeC/EGP (Sweden)
ISCO/EGP/ESeC (Germany)
ISCO/ESeC/PCS (France)
NS-SEC/version of ESeC/ESeC (United-Kingdom)

7. « Criterion validation » studies carried out by the consortium (Oct 2004, Jan 2006) (3)
The studies refer to different variants of Goldthorpian model.
In some countries, reaserchers are interested in the tasks themselves (Sweden) such as autonomy or assets specifity
whereas in other researchers introduce other elements such as the type of working contract (France, Germany)

8. « Criterion validation » studies carried out by the consortium (Oct 2004, Jan 2006) (2)
All these studies answer problematics relatively far from each other. In an explicit manner with some (Swedish and Germans) more implicit with others (English and French), the validations consist essentially in comparing ESeC to already existing classifications
ESeC/EGP (Sweden)
ISCO/EGP/ESeC (Germany)
ISCO/ESeC/PCS (France)
NS-SEC/version of ESeC/ESeC (United-Kingdom)

9. Criterion validation » studies carried out by the consortium (Oct 2004, Jan 2006) (3)
- The variety of statistical methods matches the one of the problematics.
descriptive methods or univariate regressions (United-Kingdom, Sweden)
Automatic classifications (cluster analysis) (France)
Mixte methods (many univariate analyses reasonably combined ) (Germany)

10. Criterion validation » studies carried out by the consortium (Oct 2004, Jan 2006) (4)
- Moreover, the fields of the studies are different.
The studies are also different at geographical level. French and Germans have worked on national data, Swedish and English have based themselves on European data on rather small samples .
populations studied vary from one study to the other . Thus the self-employed are almost systematically excluded from 3 out 4 analyses ;

11. Criterion validation » studies carried out by the consortium (Oct 2004, Jan 2006) (5)
- Finally, the results are very difficult to compare, but nevertheless a prototype ESeC is proposed on that basis, close to the UK classification (except the question of agriculture occupations better taken into account in ESeC)
- We are asked to comment on it.

12. PART TWO : HOW CAN WE TEST PROTOTYPE ESeC
PART TWO : HOW CAN WE TEST PROTOTYPE ESeC ? METHOD CARRIED OUT IN THE CASE OF FRANCE

13. THE METHOD : GENERAL IDEAS
On the national data, first generate a potential ESeC, that is to say we group occupations into categories as homogeneous as possible in terms of multidimensional Employment Relationships (or other aspects of socio-economic positions).
Then check whether this potential ESeC obtained on national data is similar to prototype ESEC or how different it is.

14. Occupations as the basic unit for analysis (1)
In this exercise, occupation is defined as a combination of
[ISCO minor group  occupational position ]
More precisely use :
- three digits ISCO-88(COM)
- occupational position in 5 categories
(1) self-employed (> 10 employees )
(2) self-employed ( 1 to 9 employees )
(3) self-employed no employee
(4) Employee with supervisory functions
(5) Employee without any supervisory functions
Remarks : all these categories are the one recommended in the derivation matrix to construct prototype ESeC

15. Occupations as basic unit of analysis (2)
Examples of occupation groups
131_1 = all managers of small enterprises with more than 10 employees
614_4 = all forestry and related workers who are supervisors
232_5 = all secondary education teaching professionals who are not supervisors
In total, about 750 (=150 *5) groups of occupations,
among which are 300 are not emptied l

16. The grouping of occupation groups (1)
Since we want to construct potential ESeC groups as a partition of [occupation  position ] decide about the groupings on the basis of a proximity criterion in the space of Employment Relations (but it could be on a larger criteria)

17. The grouping of occupation groups(2)
Take the mean of every single ER (or socio-economic) variables across individuals belonging to each group of occupations
[ISCO-3  occupational position ]
General ideas : occupations have to be grouped if they share the same caracteristics that is to say when they are associated with the same set of ER (or socio-economic) variables (the same means) .
The method applied :
=> CLUSTER ANALYSIS to construct potential ESeC
And finally comparison with prototype ESeC, analysis of differences

18. PART THREE WHAT DATA AND VARIABLES ARE USED TO TEST PROTYPE ESeC IN THE CASE OF FRANCE ?

19. What data sources are used to generate ESeC in the case of France ?
(1) Working Conditions Survey (supplementing the LFS)
(2) Labour Force Surveys
respondents working, aged 18-65
Allow to code :
ISCO-  3 digits using national classification (PCS) and NACE
occupational status (5 values)

20. Variables related to employment relationships (ER) and other variables
(A) Occupational position
(B1) Autonomy (opposed to subordination)
(B2) « Assets specificity » (skill, competencies) = ER
(B3) Type of labour contract
(C) Other elements related to occupation
(D) Income
(E) Education

21. Variables on occupational position (1)
employment status
self employed / employed
if self-employed :
no employee
1 or 9 employees
10 or more employees

22. Variables on occupational positions (2)
position in the company / organisation hierarchy
managerial functions (director or one of his direct associates)
having employees under one’s authority
when having employees under one’s authority , influence on their wage increase and career development

23. Variables opposing autonomy to subordination (1)
working time schedule
time schedule fixed by company / employer
working hours checked by immediate superior
no control over working hours

24. Variables opposing autonomy vs subordination (2)
pace of work dependent on :
automatic speed of a machine or a moving product
direct control of the boss
work done by colleagues
direct demands from people (customers..) or production norms

25. Variables opposing autonomy vs subordination (3)
tasks contents
tasks order and methods decided by superiors
strict compliance with orders

26. Variables on « assets specificity » (skill, competencies) (1)
occupation cognitive contents
choice of methods by oneself
complex (=non repetitive) tasks
solving unforseen problems on one’s own
interruption of the activity decided freely

27. Variables on « assets specificity » (skill, competencies) (2)
relational aspects of the job
exchanges with superiors in case of problems
exchanges with outside people in case of problems

28. Variables on work contract, for employees (1)
type of contract
permanent contract (vs fixed term, agency contract, apprenticeship)
work for public sector (vs private sector)
part-time job
annual extra payments

29. Variables on work contract, for employees (2)
carreer prospects
number of years in the company / organisation > 2 years
for those in the same company for 2 yrs, income increase
training paid by the employer over the past 3 months
job search due to the risk of losing one’s job

30. Other variables related to occupation
dealing with people that are not employees (customers, passengers, pupils, patients)
watching machine
working on a production line
painful or tiring positions, carrying heavy loads
size of the local unit, establishment
- less than 5 employees
- more than 50 employees

31. Variables on income, for employees
wages
net total monthly income
0 – 900 euros
900 – euros
1 200 – euros
1700 – euros
2 300 – euros
3 000 – euros

32. Variables on education
degree level
level I and II, level III, level IV, level V, level Vb, level VI

33. PART FOUR MAIN RESULTS

34. « Criterion validation » studies (2)
Two classifications are presented :
(1) A classification based on Employment relations (ER) variables
Field : employees only
(2) A classification based on socio- relations variables
Field : persons working

35. A classification on employees based on Employment relations (ER) (1)

36. A classification on employees based on Employment relations (ER) (2)

37. A classification on employees and self employed, based on socio-economic variables (2)

38. Conclusions of cluster analysis, in the case of France (1)
many other ESeC would do better than prototype ESeC to capture employment relations or other socio-economic dimensions ;
distinction between class 8 and 9 on the basis of ER is not totally convincing ;
the existence of a class of supervisors is not attested ;
ER criteria might be not robust enough or the variables not as numerous as they should be ;
when dealing with ER + education + income + occupational status a class of higher managers might appear but it is distinguished with the variable related to supervising functions

39. Conclusions of cluster analysis, in the case of France (2)
ER criteria might be not robust enough to create classes;
when dealing with ER + education + income +
1) occupational status a class of higher managers might appear but it is distinguished with the variable related to supervising functions
2) All self-employed are grouped in the same categorie which prove that criteria for self-empoyed should enriched.

40. PART FIVE CONCLUSIONS : ADVANTAGES AND LIMITS OF THE METHOD PROPOSED BY INSEE ?

41. Advantages of the method
multivariate analysis is in adequacy with multidimensional aspects of socio-economic positions ;
multivariate analysis could be tried in other countries taken separatly and in the EU as a whole ;
graphic representations would allow to understand the distance between countries ;
the test does not rely on a « super » survey with many variables but as the work is carried out on agregate groups of occupations, not on individuals, different surveys can be used in the same exercise provided of course ISCO and occupational position are available;

42. Limits of the method
It does not take into account heterogeneity inside groups of occupations.
it does not say what dimensions have to be included to approach socio-economic positions, we have to decide by our own ;

43. THANKS FOR YOUR ATTENTION