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Methodological issues

Methodological issues. At the end of the last meeting, some questions remained without answer. - Top down logic ( In this logic, we are able to propose 3 or 4 prototypes (Level I) which resume main issue that must be discussed in the earlier stages (those conducted in 2006 and 2009) or

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Methodological issues

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  1. Methodological issues

  2. At the end of the last meeting, some questions remained without answer • - Top down logic (In this logic, we are able to propose 3 or 4 prototypes (Level I) which resume main issue that must be discussed in the earlier stages (those conducted in 2006 and 2009) • or • -Bottom-up logic (Empirical, with a preliminary step, with a basic classification with core variables (Isco 2, status and maybe activity) • - With only statistical variables in the employment field. • But what variables ? Méthodology

  3. First results of the questionnaire sent to NSIsThe important variables are : • 1) Occupation/ profession : (DK, ES, FI, FR, HU, IE, PT, UK, CH) • 2) status (self-employed/ employeed) : AT, DK, ES, FI, FR, HU, IE, NL, PT, SE, UK, CH • 3) skill/ qualification : AT, DK, FR, HU, PT, SE, UK, CH • And only after: income,level of education and employment relationships Méthodology

  4. And about some questions Does your classification isolate ? Méthodology

  5. First conclusions • * The result of this questionnaire indicates clearly that the key variables are • occupation (and with Isco there is also qualification) • And status • * An other conclusion, some populations are isolated, identified (farmer, blue collar, etc….) • * It is important to consider these problems from the beginning of the process when building prototypes. And not at the end. Méthodology

  6. FIRST CONCLUSION • - Also the top down logic seems to be the good solution with two core variable to build the first level of the classification (Isco and status) • Do you agree? • - But, after, the bottom up logic can be used to build a second level Méthodology

  7. Data used • - Tabulation of 1st quarter of LFS 2011 with Isco08 but only 24 countries (without UK, Ireland and Hungary) • - Micro data from LFS 2009 with Isco88, ESEC and Isc08 with conversion table (without Germany, Sweden, Finland, Ireland, Malta and Slovenia) Méthodology

  8. Illustration in Europe Distribution of employed workers in 2011 according to Isco 08 in Europe (24 countries) Méthodology

  9. A wide variety of distribution by country, for Isco….. Note : In 2011, Major group 4 represented 4% of the employed people in Romania and 13% in Germany Méthodology

  10. But also a wide variety of distribution by country, for status Proportion of self-employed in the population Note : In Greece, self-employed account for 36% of population Méthodology

  11. Examples of prototypes (only level 1) • -Warning: it is only as examples to illustrate the method • We only use Isco (mainly at 1 digit, incidentally 2-digit) and status, for this level 1 • - But, in a second time, we can use, for level 2 other variables (activity, Isco 3 digit, size of firm, supervision) Méthodology

  12. Some important issues • Should we isolate, at this level1, farmers (self employed)? • (1% Sweden, 20% in Romania, 11% in Poland)? • What happens for business managers? With Isco, small business managers are classified in Isco 5 (shopkeepers) or in Isco 7 (Craftsman of the construction) or Isco6 (farmers). But, if the size of their firm is greater than 10, they are in Isco 1 with employed managers. Option prototyp 2 we do as in Isco, we separate small anf greater managers Option prototype 3, we group all managers in the same class . • How should we split up the employed in Isco 4,5,6,7,8 et 9? • How many skill levels? Two or Three? • For ESEC, for example, there are ESEC 6 , ESEC 7 and 8, and ESEC9 (unskilled) • Is it always necessary to separate blue collars and white collars? • What to do with students, pensioners, unemployed, inactive? • How many positions for the first level of classification? Only 6 or 10? Méthodology

  13. Example prototype 1 : Directly inspired by Isco • Farmers (Isco=6, Status=Self-employed) • Craftsmen, shopkeepers, Business managers (Isco =1, 2, 3, 4, 5, 7, 8 or 9, Status= Self-employed) • Managers (Isco=1, Status= employed) • Professionals (Isco=2, Status= employed) • Technicians and associate professionals (Isco=3, Status = employed) • Skilled white collars (Isco=4 or 5, Status = employed) • Skilled blue collars(Isco=6, 7 or 8, Status = employed) • Unskilled (Isco=9, Status= Employed) • For Armed forces occupations(Isco 0),Isco01 in Professionals, Isco02 in Technicians and associate professionals and Isco03 in Skilled white collars • This prototype is easy to explain with ISCO. We isolate farmers. We group all other self employed. For employed, we use only Isco 1 digit. The unskilled ( blue collars and white collars) are in one single class. Méthodology

  14. Example prototype 2 : Inspired by ESEC • Farmers (Isco=6, Status=Self-employed) • Craftsmen, shopkeepers,(Isco = 3, 4, 5, 7, 8 or 9, Status= self-employed) • Managers and professionnals (Isco =1 ou 2) • Technicians and associate professionals (Isco=3, Status = employed) • Highly qualified white collars (Isco=4, Statut= employed) • Skilled white collars (Isco= 5, Status= self employed) • Skilled blue collars (Isco=6, 7 or 8, Status = employed) • Unskilled (Isco=9, Status= Employed) • For Armed forces occupations (Isco 0), Isco01 for Managers andProfessionals, Isco02 for Technicians and associate professionals and Isco03 for Highly qualifiedwhite collars • This prototype differs from the previous one because it groups higher level managerswith manaqers and professionnals. It also distinguishes 3 levels for Isco 4,5,6,7,8,9. Méthodology

  15. Example prototype 2 bis: Also inspired by ESEC • About ESEC, we made the following exercise : • – In LFS 2009 data (21 countries) we have Isco 88, Isco08 ( with conversion table) and ESEC (built with Isco88) . • By crossing Isco08 (2 digit) and status we try to empirically • build ESEC (9 class) • *we must combine ESEC1 (Higher wage earners) et ESEC 2 (Lower wage earners) • * It is impossible to identify ESEC 6 (lower supervisory and lower technician occupations) without supervision variable. • This prototype 2 bis differs slightly from the previous one (prototype 2) • (A note, in French, is available on this exercise). • Ideally, developers of ESEC will do themselves a proposal for a prototype (via University of Trente, for example) Méthodology

  16. Example prototype 3 : Blue and white collars are well separated • Farmers (Isco=6, Status=Self-employed) • Craftsmen, shopkeepers, Business managers (Isco =1, 2, 3, 4, 5, 7, 8 or 9, Status= Self-employed) • Managers and professionnals(Isco=1 or 2, Status= employed) • Technicians and associate professionals (Isco=3, Status = employed) • Skilled white collars (Isco=4 or 5, Status = employed) • Skilled blue collars(Isco=6, 7 or 8, Status = employed) • Unskilled white collars (Isco=91, 95, Status= employed) • Unskilled blue collar (Isco=92, 93, 94, 96, Status= employed) • For Armed forces occupations (Isco 0), Isco01 in Managers and Professionals, Isco02 in Technicians and associate professionals and Isco03 in Skilled white collars • This prototype differenciates blue and white collars at all skill levels. Méthodology

  17. But we can imagine other prototypes And we can also think about a level 2 which allows consensus Méthodology

  18. Relevance criteria • What criterias, in the field of employment, can we use to assess the relevance of prototypes? • status • Qualification • Level of education • Stability of employment • Position on the salary scale (only for employed) • Supervision • And some stability over time Méthodology

  19. Example with variables in LFs 2009 • Status  Status • Qualification  Isco • Level of education  Hatlevel • Stability of employment  Temp (permanent or temporary), • Seniority in current job  Startime (seniority • Position on the salary scale (only for the employed)  INDECIL • Supervision  SUPV • And some stability over time  ?? • Is it homogeneous within one modality? Méthodology

  20. Example : Importance of temporary employment among unskilled More important for unskilled blue collar than for unskilled white collar Méthodology

  21. Example : Informations that can be made with the variable "activity". • We checked whether the sector brings informations on the quality of employment with 2 variables, Temp (permanent or temporary) and Startime (seniority in the present job) The share of temporary job in wage employment in Europe (27) in 2011 by section of Nace (1 digit) Méthodology

  22. Example : Informations that can be made with the variable "activity". (continued) Average seniority in the present job, in 2009, by section of Nace (1 digit) Méthodology

  23. Example : Informations that can be made with the variable "activity". (continued) Turn-over by sector in Europe (LFS 2009) Méthodology

  24. Bottom-up method (only with Isco 2 digits and status) • We propose to use the top-down logic to build prototypes. Nevertheless, we explored the bottom-up method to build a Level 3. This level will be useful to analyze the homogeneity of each class of a prototype. Also, we crossed Isco 2 digits and status to build that level 3. • - In E-U (27 countries) : 215 millions (employed or sel-employed) (LFS 2011) • - In E-U (24 countries without UK, Hungary and IE) 180 millions • - If we decide on a minimum number for a class of level 3 of 180 000 people (1/1000 of the total population), we find, by crossing status and Isco, 70 classes (41 employed and 29 self employed). These 70 classes covering 99% of employed and 96% self-employed . • (A note, in French, is available on this exercise). • For each prototype, one modality can be decomposed into finer elements with that level 3 to analyze its heterogeneity. Méthodology

  25. Insee 18 bd Adolphe-Pinard 75675 Paris Cedex 14 www.insee.fr Informations statistiques : www.insee.fr / Contacter l’Insee 09 72 72 4000 (coût d’un appel local) du lundi au vendredi de 9h00 à 17h00 Methodological issues Thank you for your attention ! Michel Amar and Monique Meron Tel. : +33 (0)1 41 17 54 65 Email : michel.amar@insee.fr monique.meron@insee.fr

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