Operationalising ESeC in Finnish statistical sources, Finnish EU SILK and Census 2000 ESeC workshop, 30 of June 2006 Bled, Slovenia

Riitta PoukkaB 2 Why Finnish EU SILK? All main variables available Information on supervisors Information on the size of enterprise Possible to compare Simple and full ESec syntax The present socio-economic classification and ESeC Current and usual activity

Riitta PoukkaB 3 Finnish EU SILK An income statistics Data sources: Interviews and administrative files In sample active persons (current activity) Object of ESeC study was SILK data of 2004 Census 2000 was used as a reference data No direct data collection, a combination of administrative files

Riitta PoukkaB 4 SILK 2004 structures by the simple and full ESeC simple full 1. Large employers, higher mgrs/ professionals 14,7 10,4 2. Lower mgrs/professionals, higher supervisory/ technicians 19,1 15,5 3. Intermediate occupations 11,8 9,5 4. Small employers and self-employed (non-agriculture) 3,6 9,7 5. Small employers and self-employed (agriculture) 4,4 3,6 6. Lower supervisors and technicians 0,6 9,5 7. Lower sales and service 15,2 13,5 8. Lower technical 12,4 11,5 9. Routine 18,3 16,8 All 100,0

Riitta PoukkaB 5 Structures by the simple ESeC SILKCensus 1. Large employers, higher mgrs/ professionals 14,79,3 2. Lower mgrs/professionals, higher supervisory/ technicians 19,118,3 3. Intermediate occupations 11,813,0 4. Small employers and self-employed (non-agriculture) 3,60,5 5. Small employers and self-employed (agriculture) 4,44,1 6. Lower supervisors and technicians 0,60,5 7. Lower sales and service 15,215,1 8. Lower technical 12,412,8 9. Routine 18,323,1 10. Unknown 3,4 All 100,0

Riitta PoukkaB 6 Finnish EU SILK of 2004 SOSS ESeC Employers +own account workers White-collarBlue- collar All AgricOtherUpp er Low er Large employers, higher managers / professionals Lower managers / professionals, higher supervisory /technicians Intermediate occupations Small employers and self-employed (non-agriculture) Small employers and self-employed (non-agriculture) Lower supervisors and technicians Lower sales and service Lower technical Routine All100

Riitta PoukkaB 7 Looking forwards (1): What will be main uses of ESeC in statistics? General information about social structures: Census, family and household statistics Living and housing conditions; study on income and consumption; social security Work life; demand and supply of occupational skills Structure of wages and salaries Etc. Specialized academic research (social layers, nutrition, participation in social activities, etc.)

Riitta PoukkaB 8 Looking forwards (2): One or two different / ESeC classifications? A standard classification is normally a hierarchy: Aggregate levels are split to further breakdowns One of basic rules is: If titles are same, also aggregates are same Is ESeC based on the simple syntax same classification as ESeC based on the full syntax? Or are they different classifications?

Riitta PoukkaB 9 Looking forwards (3): Role of administrative data sources? The sources of statistics are selected with consideration to quality, timeliness, costs and the burden on respondents (UN: Principles of official statistics) A trend is towards administrative data sources Information contents of administrative data is granted and sometimes poorer than in direct data collection Another possible trend might be Analytical classifications in research and standard classifications in statistics may diverge to different directions - hopefully not