Measuring graduate occupations and their skill requirements in Hungary

1. Measuring graduate occupations and their skill requirements in Hungary InGRID Expert workshop New skills new jobs: Tools for harmonising the measurement of occupations’ 10-12 February 2014 AIAS, Amsterdam Peter Robert, Institute for Political Science, Centre for Social Sciences HAS Zsuzsanna Veroszta, Educatio Nonprofit LLC

2. Outline • Graduate follow up system in Hungary • Methods for measuring occupations • Characterizing graduateoccupations from the perspective of educational requirements - combining an objective and subjective approach - examples from national and comparative datasets,young early career graduates and population level • Lessons and limitations • Further issues and challenges

3. Graduate Career Follow up System Correspondents: • Professional and methodological centre (Educatio):Support HEI projects, provide the standard of tracking system, helpdesk, national surveys, database building, research, communication, administration • HE institutions: Establish and improve graduate tracking system, adapt to national system, surveys, institutional background, external and internal communication of results, maintenance • Financial resources: European Union - Social Renewal Operative Programme 4.1.3. • Official background : Ministry of National Resources

4. Methodology of career tracking Methodology: • On-line data collection at institutional level (via e-mail from administration system) • Centralized standards • Population: students (all) and graduates 1, 3, 5 years after graduation – every spring Questionnaire: • core questionnaire completed with specific institutional questions • international standards for comparability (CHEERS, Reflex, Hegesco)

5. Data collection in career tracking: extent and characteristics • Annually since 2010 • 32 HE institutions (~90 per cent of student-population covered) • Population: graduates 1,3,5 years after graduation: ~150.000 • Amount of data: ~25.000 responders per year • Response rate: ~17 per cent • Weighting criteria: year, gender, field of study, type of the programme • Database integration: 2011-2012

6. Measuring occupationin career tracking • Open questions: on-line data collection vs. F2F / capi combination (with loading large underlying dataset) • Primary information on occupation: self-report of occupation (supported with examples) • Secondary information on occupation: employment status, form of employment (contract), sector of employment, working hours, subjective matching (vertical and horizontal) etc. • Coding occupations: manual coding of individual responses to 4 digit codes into FEOR (= Hungarian version of ISCO)+ objective index of horizontal matching (based on a comparison of occupation and the training programme)

7. FEOR – ISCO cross-matching

8. Theoretical framework Labour-market oriented approach (vs. HE oriented) (Elias, P.-Purcell, K. 2013) Diversification, heterogeneity in HE and its consequences on labor market adaptation (Clark, B. R. 1996) (Huisman, J. 1995) Changes in the concept and measurement of graduate employment (Teichler, U. 1998, 2009) (Allen, J.–van der Velden, R. 2007) Combining objective and subjective indicators characterizing graduate occupations (Abele, A. E.-Spurk, D.-Volmer, J. 2011)

9. Data • Database 1: Hungarian Career Tracking System, aged ~21+ • graduates in 2007-2011, N=45,348 • selection: employed in graduate-occupations (FEOR 1-2-3, 2 digit) N=15,473 (objective indicator) & 13,147 (subjective indicator) • Database2: European Social Survey, aged 15+ • For objective indicator: pooled data from round 2-4 (2004-2008)N=142,629 • For subjective indicator: pooled data from round 2 & 5 (2004, 2010)N=81,937 • selection: employed in graduate-occupations (ISCO 1-2-3, 2 digit)N=43,946(objective indicator) & 13,696 (subjective indicator)

10. Indicators of educational heterogeneity of graduate occupations

11. Objective measurement of heterogeneity of educational input Highest value of the adjusted standardized residuals, based on the proportions taken from an occupation (FEOR 08 – 2 digit ) by field of study table, N=15,473

12. Subjective measurement of horizontal matching Ratio of subjective horizontal match in graduate occupations by 2 digit FEOR 08 categories N=13,147

13. Subjective horizontal match and objective educational heterogeneity of graduate occupations (FEOR 2 digit codes) Mismatch and educationalhomogeneity Match and educationalhomogeneity Highest value of Adj. S.Resid.

14. Objective measurement of heterogeneity of educational input Highest value of the adjusted standardized residuals, based on the proportions taken from an occupation (ISCO88 – 2 digit ) by field of study table, N=43,946

15. Subjective skill level requirements for graduate occupations Means of required time of learning for someone with right qualification to complete the work in days by 2 digit ISCO 88 categories, N=13,696

16. Objectiveeducationalheterogeneity and subjectiveskillrequirements of graduateoccupations (ISCO 88 2 digitcodes) High level skill requirements and educational heterogeneity

17. Lessons and limitations In Hungary for early career graduates: • educational input is more homogeneous for professionalsand particularly heterogeneous for managers • subjective horizontal match is stronger for professionals • homogeneous educational input and higher horizontal match is combinedfor professionals, while heterogeneous educational input and lower horizontal match go together for associate professionals Graduates from comparative population data • less clear pattern for professionals and associate professionals but educational heterogeneity for managers is present • subjective skill level requirements are lower for associate professionals • high level of skill requirement go together with educational heterogeneity for managers and with educational homogeneity for professionals Limitations • only descriptive picture provided, no multivariate analysis yet • in case of the population data: no control for age, country variation is not studied / presented

18. Further plans, open questions to discuss Measurement • Elaborating on occupational classification: how detailed can it be? (2-3-4 digit coding) number of observations as a barrier • How much is the objective indicator based on the standardized adjusted residuals sensitive to the size of the table (number of categories in ISCO /field of study) • The role and function of subjective indicators in the analysis?(also from the perspective of employer) • Does educational requirement analysis disclose coding discrepancies More theory (for graduate occupations) • Educational input behind job: - what is the role of the structural changes in the HE system? (Bologna process)- what is the consequence of mass HE? Is the level of HE based accumulated skills and qualifications on the decline? • How do LM needs affect skill requirements of the job?- Option 1: LM needs better skilled graduate employees due to the technological change- Option 2: LM dos not need better skilled graduates, only required competencies are: language skills, good use of computer, ability of working in team, accepting high work load and monotony in the job

19. Thank you and comments welcome Peter Robert,robert.peter@tk.mta.hu Zsuzsanna Veroszta, veroszta.zsuzsanna@educatio.hu

20. References Abele, A. E., Spurk, D., & Volmer, J. (2011): The construct of career success: measurement issues and an empirical example. Zeitschrift für Arbeitsmarktforschung, 43(3) Allen, J.–van der Velden, R. (eds.) (2007): The Flexible Professional in the Knowledge Society: General Results of the REFLEX-project. Research Centre for Education and the Labour Market, Maastricht University, The Netherland Clark, B. R. (1996): Diversification of Higher Education: Viability and Change. In.: Meek, V. L.–Goedegebuure, L.–Kivinen, O.–Rinne, R. (szerk.): The Mockers and Mocked: Comparative Perspectives on Differentiation. Convergence and Diversity in Higher Education. Pergamon Press, Oxford Elias, P.-Purcell, K. (2013): Classifying graduate occupations for the knowledge society. Working Paper no.5, Futuretrack, Higher Education Careers Services Unit. Huisman, J. (1995): Differentiation, Diversity and Dependency in Higher Education. Utrecht, Lemma Teichler, U. (1998): The Transition from Higher Education to Employment in Europe. Higher Education in Europe, 23(4) Teichler, U. (2009) Higher Education and the World of Work. Sense Publishers, Rotterdam.