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Measuring the task frequencies of 430 4-digit ISCO occupational units in 13 countries

Measuring the task frequencies of 430 4-digit ISCO occupational units in 13 countries. Brian Fabo Analyst, CELSI Data and Survey Manager, WageIndicator Bratislava Kea Tijdens Research coordinator, University of Amsterdam/AIAS. 27 .1 1 . 201 3. Outline. Explanation of the challenge

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Measuring the task frequencies of 430 4-digit ISCO occupational units in 13 countries

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  1. Measuring the task frequencies of 430 4-digit ISCO occupational units in 13 countries Brian Fabo Analyst, CELSI Data and Survey Manager, WageIndicator Bratislava Kea Tijdens Research coordinator, University of Amsterdam/AIAS 27.11. 2013

  2. Outline • Explanation of the challenge • EurOccupations approach: • Methods • Results • Conclusion • WageIndicator approach • What is WageIndicator? • How is the WI survey used • Time line and data intake so far

  3. The academic challenge • Academic relevance • occupation is a key variable in social sciences • cross-country comparisons based on assumption that similar occupational titles refer to same work activities • yet, an empirical basis for this assumption is lacking • EurOccupations project • FP6 project for developing an occupations database for comparative socio-economic research in European Union (2006–09): BEL, DEU, ESP, FRA, GBR, ITA, NLD, POL • Research Objective: Are occupations similar regarding work activities >> Does an Italian plumber engage in the same activities as a plumber from France, Poland or UK?

  4. EurOccupations methods • Measuring 10 tasks for 160 occupations • 160 occupations selected from the EurOccupations database with1,440 titles • Selection: variation in skill level, gender composition, number of jobholders, coverage of entire labour market • Drafting 10 task descriptions (desk research) per occup. • Raters: experts and job-holders • Rating task frequency on a 5 point scale (never ..... daily) • Recruiting experts through networks EurOccupations teams (2468 experts rated 2950 occupations) • For some occupations no experts - > recruiting jobholders through national WageIndicator websites (1247 raters)

  5. Results • Q1 Are occupations similar? • 51% of occupations lack of agreement or no agreement • 38% weak or moderate agreement • 12% strong agreement • Q2 Are occ’s similar within countries? • Spain 80%, Germany 58%, Netherl. 43%, Poland 48% • Q3 Are occ’s similar across countries? • Spain strong agreement, Germany weak agreement • Poland and Netherlands lack of agreement • Q4 Rate experts and jobholders similar? • Experts: 35% of occ’s at least moderate agreement • Jobholders: 50% of occ’s at least moderate agreement

  6. EurOccupations conclusions • Content • We assume that occupations are similar across countries, but to a large extent they are not -> how to explain? • Methods • Empirical testing of task descriptions can be undertaken for a wide range of occupations across Europe, particularly when recruiting jobholders through Internet • Empirical testing of skill requirements can be undertaken for a wide range of occupations across Europe, but needs skill lists per occupation -> needs further development • Further research may even allow for an empirical underpinning of occupational dynamics: which processes are underlying the occupational formation?

  7. What next: How to collect data on tasks? • WageIndicator Web Survey • WageIndicator survey – Covers 80 countries, gathers 1000s of cases monthly • Asks for occupation, using an extended version of the EurOccupations database(all occ’s coded according to ISCO08) • In addition to wage questions about working conditions, socio-economic characteristics • Tasks lists were included in the web survey, these show up depending on ticked occupation, asking to tick frequency on a 5-pt scale

  8. Measurement of task frequency • Tasks lists in the Web Survey • Tasks taken from ISCO-08 description of tasks for all 4-digit occupational units (in English) • Tasks lists for 427 of 433 units were prepared • For 6 so-called ‘not-elsewhere-classified occupations’ no descriptions were included • In total 3237 tasks (including few duplicates)on average 7.58 tasks per occupation • Tasks were translated into 7 languages for 13 countries

  9. Time line • November 2013: data collection started in: • Argentina, Australia, Belarus, Belgium, Brazil, Indonesia, Kazakhstan, Mexico, Netherlands, Russia, South Africa, Spain, UK • April-May 2014: first analysis

  10. Data Intake • Up to January 2014 (included) the data intake is as follows:

  11. Thanks for attention Brian.Fabo@celsi.sk k.g.tijdens@uva.nl

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