1. Introduction to LFS from a research perspective Christof Wolf, Andrea Lengerer, Heike Wirth German Microdata Lab, GESIS

2. Strengths of OS microdata Samples are usually very large  Allowing for analysis of small groups  Allowing for analysis of small regions  Leading to higher precision Question programs are usually relatively stable  Supporting comparison over time  analysis of social change For surveys regulated at European level procedures and (target) variables are partly standardized  Supporting cross-national analysis Often high response rates (participation sometimes compulsory) 18 Sept. 20144th DwB Trainings Course2

3. Research done with LFS As a reference statistic Substantive research, e.g. The Effects of Labour Market Regulations Being a Eurpean wide repeated cross-sectional survey LFS allows analysising the development of the labour market in a comparative perspective. One example is the effect of changing employment protection legislation on age-specific labour market participation. 18 Sept. 20144th DwB Trainings Course3

4. Research done with LFS e.g. Migration and Integration LFS offers possibility to conceptualize immigration by nationality and/or by country of birth and allows to differentiate between immigrants obtaining their education in their country of residency or abroad (through years of residence) But: nationality and country of birth are both coarsened in the user data base 18 Sept. 20144th DwB Trainings Course4

5. Example 1: Hermes & Leicht 2010* Research Question: „The aim of our analyses is to evaluate country specific differences and similarities in the scope and characteristics of immigrant entrepreneurship. The analyses are expected to highlight the importance of macro-level factors, namely opportunity and institutional structures.” Data: EU-LFS 2005 18 Sept. 20144th DwB Trainings Course5 * Kerstin Hermes and René Leicht, 2010: Scope and Characteristics of Immigrant Entrepreneurship in Europe. Working Paper, Mannheim.

6. Defining Immigrant Groups Authors base their definition of ‘immigrant’ on nationality because nationality and not country of birth matters from a legal point of view Further differentiation of non-nationals in: Foreigners from other EU countries and from Non-EU countries 18 Sept. 20144th DwB Trainings Course6

7. Self-employment Rates 18 Sept. 20144th DwB Trainings Course7 Poland

8. Self-Employment Rates in Europe by country of birth 18 Sept. 20144th DwB Trainings Course8

9. Example 2: Methodological Possibilities LFS is a cross-national repeated cross-section for European  Analysis of social change, Age-Period-Cohort analysis  Multi-level modeling; cross-classified level 2 units: countries x time  Alternatively: two-step modelling approach  Country specific individual level modelling of interesting dependent variable, e.g. employment status  Cross-country analysis of results from step 1, e.g. predicted probabilities 18 Sept. 20144th DwB Trainings Course9

10. Time series used by Dieckhoff & Steiber 18 Sept. 20144th DwB Trainings Course10 Martina Dieckhoff and Nadia Steiber, 2012: Institutional reforms and age-graded labour market inequalities in Europe. International Journal of Comparative Sociology Online prepublication.

11. Predicted probabilities for fixed-term employment 18 Sept. 20144th DwB Trainings Course11

12. Comparability of LFS data I.Comparability of design II.Comparability of variables III.Comparability over time 18 Sept. 20144th DwB Trainings Course12

13. I. Comparability of Design

14. Sampling & Weighting 1 Mostly last censuses or population registers are used as frame (LU: list of telefon numbers) Depending on country final sampling untits are persons, households, dwelling units, cluster of dwelling units or addresses Sampling rate per quarter varies from 0.24% (TR) to 3% (IE) Sex, age and region are used for adjustment weights; some countries also consider nationality, ethinic background, household size, employment status etc. 1 Data from 2009 18 Sept. 20144th DwB Trainings Course14

15. Field Work 1 LFS is conducted in different survey modes; often in mixed-mode; mostly CAPI/PAPI but also self- administered and telephone interviews Workload of interviewers varies from 50 (PL) to 1,125 (NL) to interviews per quarter 18 Sept. 20144th DwB Trainings Course15 1 Data from 2009

16. Proxy Interviews 1 EU regulation allows that information on household members is provided by other household members  proxy interviews EU average is 34 % (unweighted) but proxy rates vary from 2% (DK) to 58% (SI, TR) 18 Sept. 20144th DwB Trainings Course16 1 Data from 2009

17. Response Rates and Coverage Participation in LFS is compulsory in some and voluntary in other countries Large variation in response rates: 31 % (LU) to 97 % (DE) (rates may not be stricly comparable) Institutional households and persons over 74 are not covered in all countries (UK & IS only from 16) 18 Sept. 20144th DwB Trainings Course17

18. II. Comparability of Variables

19. Ex-ante Output Harmonization The regulation defines the mandatory variables for EU-LFS 18 Sept. 20144th DwB Trainings Course19

20. 18 Sept. 20144th DwB Trainings Course20

21. Ex-ante Output Harmonization The regulation defines the mandatory variables for EU-LFS These are so called target variables Data do not have to come from surveys but may come from administrative records and registers No common questionnaire Survey questions are not standardized/ harmonized  large variation 18 Sept. 20144th DwB Trainings Course21

22. Example 1: Marital Status Italy Hungary Croatia 18 Sept. 20144th DwB Trainings Course22

23. Example 1: Marital Status ITHUHR Single Married Separated de factoWidowed Lagally separatedDivorced or legally separatedDivorced Cohabitating couple WidowedSeparated from spouse User Data Base 0 Widowed, divorced or legally separated 1 Single 2 Married 18 Sept. 20144th DwB Trainings Course23

24. Example 2: Supervisory Status 2 Part of ‘quality-in-work’ indicators used to monitoring gender equality in the labour market Supervisory status also used in measures of socio- structural / class position, e.g.  Ericson/Goldthorpe/Portocarero schema (EGP)  Wright’s class schema  European Socioeconomic Classification (ESeC) 2 Reinhard Pollak, Heike Wirth, Felix Weiss, Gerrit Bauer and Walter Müller. 2009. On the Comparative Measurement of Supervisory Status using the Examples of the ESS and the EU- LFS. In International vergleichende Sozialforschung. Ed. Birgit Pfau-Effinger, Sladana Sakac Magdalenic and Christof Wolf,. Pp. 173-206. Wiesbaden: VS Verlag für Sozialwissenschaften. 18 Sept. 20144th DwB Trainings Course24

25. ESeC classes 1.Large employers, higher managerial and professional occupations 2.Lower managerial and professional occupations 3.Intermediate occupations 4.Small employers and own account workers 5.Employers and self-employed in agriculture 6.Lower supervisory and lower technician occupations 7.Lower services occupations 8.Lower technical occupations 9.Routine occupations Supervisors are assumed to be different in their employment relations to ‘rank and file’ workers 18 Sept. 20144th DwB Trainings Course25

26. ESeC classes 1.Large employers, higher managerial and professional occupations 2.Lower managerial and professional occupations 3.Intermediate occupations 4.Small employers and own account workers 5.Employers and self-employed in agriculture 6.Lower supervisory and lower technician occupations 7.Lower services occupations 8.Lower technical occupations 9.Routine occupations Supervisors are assumed to be different in their employment relations to ‘rank and file’ workers Supervisory status used to allocate employees otherwise coded as ESeC 3,7,8,9 into ESeC 2 or 6 18 Sept. 20144th DwB Trainings Course26

27. Supervisory Status: Concept EU-LFS (explantory notes): “A person with supervisory responsibilities takes charge of the work, directs the work and sees that it is satisfactorily carried out” EU-SILC (description target variables): “Supervisory responsibility includes formal responsibility for supervising a group of other employees (...), whom they supervise directly, sometimes doing some of the work they supervise” ESeC Draft User Guide: “Supervisors are neither managers nor professionals but are responsible as their main job task for supervising the work of other employees” 18 Sept. 20144th DwB Trainings Course27

28. 18 Sept. 20144th DwB Trainings Course28 Operationalisation of the ‚supervisory status‘: LFS – Examples

29. 18 Sept. 20144th DwB Trainings Course29 Operationalisation of the ‚supervisory status‘: LFS – Examples

30. 18 Sept. 20144th DwB Trainings Course30 Supervisory Status: LFS 2010 in % How comparable are these figures?

31. III. Comparability over Time

32. Availability of microdata Eurostat’s LFS microdata starts from 1983 Data for EU countries are usually available depending on when they joined the EU, and from 2000 for all countries Germany (anonymised microdata is provided from 2002 onwards only) and Malta (anonymised microdata is provided from 2009 onwards only) are exceptions For Iceland and Norway data are available from 1995 For Switzerland data are available from 1996 18 Sept. 20144th DwB Trainings Course32

33. Reasons for limited comparability over time 1.Changing reference period, annual vs. continuous survey 2.Changing classifications 3.Changing codification 4.Changing sample design 18 Sept. 20144th DwB Trainings Course33

34. (1) Changing reference period Annual surveys from 1983 to 1997 (conducted in spring) Continuous surveys starting in 1998 (reference weeks are spread uniformly throughout the year) Data for all quarters of a year are progressively available starting between 1998 and 2004 for all countries, except Germany (quarterly data are available from 2005) The reference sample for yearly files corresponds to one reference quarter in spring until 2004, and to an annual sample covering all weeks of the year from 2005 18 Sept. 20144th DwB Trainings Course34

35. Availability of microdata since… 18 Sept. 20144th DwB Trainings Course35 countryyearlyquarterlycountryyearlyquarterly AT BE BG CH CY CZ DE DK EE ES FI FR GR HU IE Austria Belgium Bulgaria Switzerland Cyprus Czech Republic Germany Denmark Estonia Spain Finland France Greece Hungary Ireland 1995 1983 2000 1996 1999 1997 2002 1983 1997 1986 1995 1983 1996 1983 1999 2000 2010 2004 1998 2005 1999 2000 1996 1998 2003 1998 1999 IS IT LT LU LV MT NL NO PL PT RO SE SI SK UK Iceland Italy Lithuania Luxembourg Latvia Malta Netherlands Norway Poland Portugal Romania Sweden Slovenia Slovak Republic United Kingdom 1995 1983 1998 1983 1998 2009 1983 1995 1997 1986 1997 1995 1996 1998 1983 2003 1997 2002 2003 2002 2009 2000 1997 1999 2001 1999 1998 2000

36. (2) Changing classifications 18 Sept. 20144th DwB Trainings Course36 RegionNUTSNUTS II (except for AT, DE and UK), several changes Economic activity NACENACE Rev. 2 from 2008 NACE Rev. 1.1 from 2005 to 2008 NACE Rev. 1 from 1992 to 2004 NACE 1970 from 1983 to 1991 OccupationISCOISCO 08 from 2011 ISCO 88 COM until 2010 EducationISCEDISCED 1997

37. (3) Changing code schemes Two examples: Nationality Education 18 Sept. 20144th DwB Trainings Course37

38. Nationality 18 Sept. 20144th DwB Trainings Course38 NATIONAL, until 2003NATIONAL, from 2004 onwards 0 111 911 800 National / Native of own Country EU15 Non EU15 Non-National / Non-Native (in case the distinction EU/Non-EU is not possible) No answer, suppressed, other country or stateless 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 National / Native of own Country EU15 NMS10 (10 new Member States of 2004) NMS2 (2 new Member States of 2007) NMS12 (code 2,3) EU27 (code 1,2,3) EFTA Other Europe Europe outside EU27 (code 6,7) North Africa Other Africa Near and Middle East East Asia South and South East Asia North Africa and Near and Middle East (code 9,11) East and South Asia (code 12,13) North America Central America (and Caribbean) South America Australia and Oceania Latin America (code 17,18) North America and Australia / Oceania (code 16,19) No answer, suppressed, other country or stateless

39. Education 18 Sept. 20144th DwB Trainings Course39 HATLEV1D, from 1983 onwardsHATLEVEL, from 1998 onwards 1 2 3 -2 Low: Lower secondary Medium: Upper secondary High: Third level No answer Not applicable (child less than 15 years) 0 10 11 21 22 30 31 32 33 34 35 36 41 42 43 51 52 60 -2 No formal education or below ISCED 1 ISCED 0-1 ISCED 1 ISCED 2 ISCED 3c (shorter than 2 years) ISCED 3 (without distinction a, b or c possible, 2 years and more) ISCED 3c (2 years and more) ISCED 3 a,b ISCED 3c (3 years or longer) or ISCED 4c ISCED 3b or ISCED 4b ISCED 3a or ISCED 4a ISCED 3 or 4 (without distinction a, b or c possible) ISCED 4a,b ISCED 4c ISCED 4 (without distinction a, b or c possible) ISCED 5b ISCED 5a ISCED 6 No answer Not applicable (child less than 15 years)

40. (4) Changing sample design Changing sampling frame (i.e. Central Population Register in LU until 2008 and random digit dialling from 2009) Changing stratification of sampling units (i.e. multi- stage stratified sample of dwellings in HU from 2003) Changing sample size (i.e. significant increase of sample size in DK in 2007) Changing age range (i.e. restriction to age 15 and over in LT before 2002) 18 Sept. 20144th DwB Trainings Course40

41. Other reasons for limited comparability Changing concepts (i.e. revised employment and un- employment definition in some countries and years) Changing questionnaires (i.e. wording and order of questions) Changing population figures used for the population adjustment (on the basis of new population censuses) 18 Sept. 20144th DwB Trainings Course41

42. Conclusion Do not take comparability for granted Make use of the available documentation, e.g. quality reports, main characteristics report, national questionnaires But don‘t forget the strengths of these data! 18 Sept. 20144th DwB Trainings Course42

43. Thank you for your attention! Contact German Microdata Lab GESIS Leibniz-Institute for the Social Sciences www.gesis.org/gml gml@gesis.org 18 Sept. 20144th DwB Trainings Course43