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Research Potential of Eurostat Micro-data

Research Potential of Eurostat Micro-data. Jo Wathan and Anthony Rafferty Centre for Census and Survey Research University of Manchester. This presentation. Describe the microdata available through Eurostat Give examples of use of EU-LFS, ECHP and EU-SILC Identify research potential.

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Research Potential of Eurostat Micro-data

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  1. Research Potential of Eurostat Micro-data Jo Wathan and Anthony Rafferty Centre for Census and Survey Research University of Manchester

  2. This presentation • Describe the microdata available through Eurostat • Give examples of use of EU-LFS, ECHP and EU-SILC • Identify research potential

  3. Why use multinational EU microdata? • To develop and extend theory • To understand national differences • For policy transfer • To provide EU-wide descriptions

  4. Identifying and understanding national differences • International comparisons can challenge what we take for granted • Women with young children work part-time • Young people leave home in their late teens to cohabit with partners

  5. Eurostat Micro-datasets • EU Labour Force Survey (EU-LFS) • European Community Household Panel (ECHP) • European Union Statistics on Income and Living Conditions (EU-SILC) • Community Innovation Survey • Continuing Vocational Training Survey • Structure of Earnings Survey (safe centre access) • Adult Education Survey (forthcoming)

  6. EU-LFS • First release of anonymised EU-LFS microdata: 2003 • Currently available: 2008 release, covering core survey data until 2007 and ad hoc module data from 2006 • Complete data 27 EU Member States except Malta from 2002 • Data for 12 new MS from 2000 • Data for Germany are included from 2002 • Quarterly and annual files for all 27 EU Member States except Malta, but including Iceland and Norway, since 1983 subject to availability

  7. EU-LFS: Harmonization Harmonization is reached by: (a) the recording of the same set of characteristics in each country (b) a close correspondence between the EU list of questions national questionnaires (c) use of the same definitions for all countries (following ILO-definitions) d) the use of common classifications (NACE, ISCO, ISCED, NUTS etc) (e) the data being centrally processed by Eurostat

  8. EU-LFS • Good for employment, education and training • Individual/household file • No income data • Panel design varies considerably by country – following rules may not give true longitudinal design at individual level

  9. Example: Working Hours (women) Source: Eurostat

  10. Example 2: Theory testingWolbers (2003) ESR 19(3) 249-266 • Used individual and country level variables and analyses • Vocational training • At school +vely related to no. of job mismatch • At workplace –vely related to no. of job mismatch • Both are +vely related to impact of job mismatch

  11. EU-LFS: Current Research Applications • Youth Unemployment • Precarious work in Europe • Atypical employment and social security • Job mobility in the EU • Pathways to work: labour market integration of young people • The impact of foreign workers on domestic unemployment • Part-time work • Women, employment, and family in Europe • Migrant women in the labour market: current situation and future prospects (in collaboration with DG EMPL) • Can childcare subsidies help explain differences in working hours across countries?

  12. ECHP 1994-2001 • Fore-runner to EU-SILC • Annual panel survey covering income, working life, housing situation, social relations, health and biographical information • Community Survey covering 14 Member States from 1994 to 2001 • 130 000 adults (16+) and 60 000 households were interviewed every year

  13. ECHP: Research Applications • Poverty dynamics • Social exclusion and material deprivation • Family dynamics • Labour market dynamics • Health and wellbeing • Health and retirement • Comparative analysis of welfare regimes

  14. ECHP: Research Example • Schuring et al 2007: • Does self-perceived poor health/ having a chronic health problem predict labour market transitions? • Transition model: Movements from employment in one year to unemployment in the next year (movements ‘between t-1and t’) • 11 EU countries compared

  15. odds ratios of perceived poor health/chronic sickness for those moving from employed to unemployed

  16. Childcare strategies of divorced mothers in EuropeRaeymaeckers et al ESR (2008) 24(1) 115-131 • Look at relationship between formal and informal childcare across countries • Testing theories of ‘crowding out’ and ‘crowding in’ • Generally points towards crowding out

  17. EU-SILC • Since 2004, Main annual European data source on income, poverty, social exclusion and living conditions • All 25 MS plus Norway and Iceland for SILC 2005 • 430 000 individuals and 200 000 households for the SILC 2005 • c. 72 variables at household level and 85 at individual level • Cross-sectional and Longitudinal data (4 year panels once matured)

  18. EU-SILC Topics (1) • Social exclusion • Arrears on housing and other payments • Difficulty in making ends meet • Consumer durables – affordability • Physical and social environment • Health status – summary definition • Access to health and dental car • 2005 Inter-generational transmission of poverty • 2006 Social Participation • 2007 Housing Conditions • 2008 Financial Exclusion and Debt

  19. EU-SILC: Laeken Indicators • SILC-based Laeken indicators include • At-risk-of-poverty rate (by age, gender, household type,…) • Persistent at risk-of-poverty rate (by age, gender) • Inequality measures from income distribution (S80/S20, Gini coefficient) • Self-defined health status by income level (by age, gender,)

  20. Measuring material deprivation in the enlarged EUWhelan et al. (2008) ESRI Working Paper • Looking for cross-national approach to deprivation • C.f. relative deprivation within country • Dimensional approach • Consumption • Residential area • Household Facilities • Consumption seems better than others

  21. The Community Innovation Statistics (CIS) • Main data source for measuring innovation in Europe covering basic information of the enterprise, product and process innovation, innovation activity and expenditure, effects of innovation. • 27 MS plus Iceland, Norway and Turkey for CIS4 (launched in 2005, reference period 2004 with observation period 2002 to 2004) • 250 000 enterprises (10 employees +) from industry and services

  22. The Continuing Vocational Training Survey (CVTS) • Data on the strategies of enterprises with respect to training of their staff (Participation rates, volumes, costs…) • 27 MS plus Norway for CVTS3 (launched in 2006, reference period 2005) • 200 000 enterprises (10 employees +) from industry and services • 160 variables collected • In spring 2008 release of CVTS3 anonymised microdata

  23. Structure of Earnings Survey (SES) • Main data source for detailed information on the level of remuneration, individual characteristics of employees and of their employer (every 4 years) • 27 MS plus Norway and Iceland for SES 2002 • 7 800 000 employees from enterprises (10 employees +) belonging to NACE C to K (C to O from 2006 onwards)

  24. The Adult Education Survey (AES) • Participation of adults in formal, non formal education and training as well as to informal training. • 27 MS plus Norway, Turkey and Switzerland for the first data collection (2005-2007) • 150 000 individuals plus countries extensions: Poland 25 000, Italy 50 000 • Forthcoming

  25. Cross-national and longitudinal microdata • Issues of definitions • Issues of meaning • Potential for some very powerful analyses!

  26. Further info • EUROSTAT: http://epp.eurostat.ec.europa.eu • Further information on ECHP and publication database, see European Panel User Network website: http://epunet.essex.ac.uk

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