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Strengths of OS microdata

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

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Strengths of OS microdata

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  1. Introductionto LFSfrom a researchperspectiveChristof Wolf, Andrea Lengerer, Heike WirthGerman Microdata Lab, GESIS

  2. Strengthsof OS microdata • Samples areusuallyvery large • Allowingforanalysisofsmallgroups • Allowingforanalysisofsmallregions • Leadingtohigherprecision • Questionprogramsareusuallyrelativelystable • Supportingcomparisonover time  analysisofsocialchange • Forsurveysregulatedat European levelproceduresand (target) variables arepartlystandardized • Supportingcross-national analysis • Often high responserates(participationsometimescompulsory) 4th DwB Trainings Course

  3. Research donewith LFS • As a referencestatistic • Substantive research, e.g. The Effects of Labour Market Regulations Being a Eurpeanwiderepeatedcross-sectionalsurvey LFS allowsanalysisingthedevelopmentofthelabourmarket in a comparativeperspective. Oneexampleistheeffectofchangingemploymentprotectionlegislation on age-specificlabourmarketparticipation. 4th DwB Trainings Course

  4. Research donewith 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 4th DwB Trainings Course

  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 * Kerstin Hermes and René Leicht, 2010: Scope and Characteristics of Immigrant Entrepreneurship in Europe. Working Paper, Mannheim. 4th DwB Trainings Course

  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 4th DwB Trainings Course

  7. Self-employment Rates Poland 4th DwB Trainings Course

  8. Self-Employment Rates in Europe bycountryofbirth 4th DwB Trainings Course

  9. Example 2: MethodologicalPossibilities • 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-stepmodellingapproach • Country specific individual levelmodellingofinterestingdependent variable, e.g. employmentstatus • Cross-countryanalysisofresults from step 1, e.g. predictedprobabilities 4th DwB Trainings Course

  10. Time seriesusedbyDieckhoff & Steiber Martina Dieckhoff and Nadia Steiber, 2012: Institutional reforms and age-graded labour market inequalities in Europe. International Journal of Comparative Sociology Online prepublication. 4th DwB Trainings Course

  11. Predictedprobabilitiesforfixed-term employment 4th DwB Trainings Course

  12. Comparability of LFS data • Comparabilityof design • Comparabilityof variables • Comparabilityover time 4th DwB Trainings Course

  13. I. Comparabilityof Design

  14. Sampling & Weighting1 • Mostly last censusesorpopulationregistersareusedasframe (LU: listoftelefonnumbers) • Depending on country final samplinguntitsarepersons, households, dwellingunits, cluster of dwellingunitsoraddresses • Sampling rate per quartervaries from 0.24% (TR) to 3% (IE) • Sex, ageandregionareusedforadjustmentweights; some countries also considernationality, ethinicbackground, householdsize, employmentstatus etc. 1 Data from 2009 4th DwB Trainings Course

  15. Field Work1 • LFS isconducted in different surveymodes; often in mixed-mode; mostly CAPI/PAPI but also self-administeredandtelephoneinterviews • Workloadofinterviewersvaries from 50 (PL) to 1,125 (NL) tointerviews per quarter 1 Data from 2009 4th DwB Trainings Course

  16. Proxy Interviews1 • EU regulationallowsthatinformation on householdmembersisprovidedbyotherhouseholdmembers proxyinterviews • EU averageis 34 % (unweighted) but proxyratesvary from 2% (DK) to 58% (SI, TR) 1 Data from 2009 4th DwB Trainings Course

  17. Response Rates andCoverage • Participation in LFS iscompulsory in someandvoluntary in other countries • Large variation in responserates: 31 % (LU) to97 % (DE) (ratesmay not bestriclycomparable) • Institutionalhouseholdsandpersonsover 74 are not covered in all countries (UK & IS onlyfrom 16) 4th DwB Trainings Course

  18. II. Comparabilityof Variables

  19. Ex-ante Output Harmonization • The regulationdefinesthemandatory variables for EU-LFS 4th DwB Trainings Course

  20. 4th DwB Trainings Course

  21. Ex-ante Output Harmonization • The regulationdefinesthemandatory variables for EU-LFS • These are so calledtarget variables • Data do not havetocome from surveys but maycome from administrative recordsandregisters • Nocommonquestionnaire • Survey questionsare not standardized/ harmonized large variation 4th DwB Trainings Course

  22. Example 1: Marital Status Italy Hungary Croatia 4th DwB Trainings Course

  23. Example 1: Marital Status 4th DwB Trainings Course

  24. Example 2: Supervisory Status2 • Part of ‘quality-in-work’ indicators used to monitoring gender equality in the labourmarket • 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 andWalter 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, SladanaSakacMagdalenicandChristof Wolf,. Pp. 173-206. Wiesbaden: VS Verlag für Sozialwissenschaften. 4th DwB Trainings Course

  25. ESeCclasses • Large employers, higher managerial and professional occupations • Lower managerial and professional occupations • Intermediate occupations • Small employers and own account workers • Employers and self-employed in agriculture • Lower supervisory and lower technician occupations • Lower services occupations • Lower technical occupations • Routine occupations • Supervisors are assumed to be different in their employment relations to ‘rank and file’ workers 4th DwB Trainings Course

  26. ESeCclasses • Large employers, higher managerial and professional occupations • Lower managerial and professional occupations • Intermediate occupations • Small employers and own account workers • Employers and self-employed in agriculture • Lower supervisory and lower technician occupations • Lower services occupations • Lower technical occupations • 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 4th DwB Trainings Course

  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” • ESeCDraft User Guide: “Supervisors are neither managers nor professionals but are responsible as their main job task for supervising the work of other employees” 4th DwB Trainings Course

  28. Operationalisation of the ‚supervisory status‘: LFS – Examples 4th DwB Trainings Course

  29. Operationalisationofthe ‚supervisorystatus‘: LFS – Examples 4th DwB Trainings Course

  30. Supervisory Status: LFS 2010 in % Howcomparablearethesefigures? 4th DwB Trainings Course

  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 4th DwB Trainings Course

  33. Reasons for limited comparability over time • Changing reference period, annual vs. continuous survey • Changing classifications • Changing codification • Changing sample design 4th DwB Trainings Course

  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 4th DwB Trainings Course

  35. Availability of microdata since… 4th DwB Trainings Course

  36. (2) Changing classifications 4th DwB Trainings Course

  37. (3) Changing code schemes Two examples: • Nationality • Education 4th DwB Trainings Course

  38. Nationality 4th DwB Trainings Course

  39. Education 4th DwB Trainings Course

  40. (4) Changing sample design • Changing sampling frame (i.e. Central Population Register in LU until 2008 and randomdigitdialling 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) 4th DwB Trainings Course

  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) 4th DwB Trainings Course

  42. Conclusion • Do not takecomparabilityforgranted • Makeuseoftheavailabledocumentation, e.g. qualityreports, maincharacteristicsreport, national questionnaires • But don‘tforget thestrengths of thesedata! 4th DwB Trainings Course

  43. Thankyouforyourattention! ContactGerman Microdata LabGESIS Leibniz-Institute for the Social Sciences www.gesis.org/gml gml@gesis.org 4th DwB Trainings Course

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