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

2. Outline Graduate follow up system in Hungary Methods for measuring occupations Characterizing graduate occupations 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 occupation in 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 ISCO 88FEOR 08 10 Legislators, senior officials and managers 11 Legislators and senior officials 11 Chief executives, senior officials and legislators 12 Corporate managers 12 Administrative and commercial managers 13 Managers of small enterprises 13 Production and specialised services managers 14 Managers of other economic units 20 Professional 21 Phys, mathem, engin science professionals 21 Science and engineering professionals 22 Life science and health professionals 22 Health professionals 23 Teaching professionals 23 Social professionals 24 Other professional 24 Teaching professionals 25 Business professionals 26 Legal and social professionals 27 Cultural, sport, arts professionals 29 Other highly qualified administrators 30 Technicians and associate professionals 31 Physical, engineering science associate prof 31 Science and engineering associate professionals 32 Life, science and health associate prof 32 Professional managers, supervisors 33 Teaching associate professionals 33 Health associate professionals 34 Other associate professionals34 Teaching associate professionals 34 Social and labour market services professionals 35 Business and administration associate professionals 36 Cultural, sport, arts associate professionals 39 Other administrators

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) Database 2: 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 ContentMeasurementSource Objective heterogeneity of educational input Highest values of adjusted standardized residuals from distribution by field of study (= higher homogeneity) Hungarian Graduate Career Tracking System (2007-2011) Subjective judgment of horizontal matching Ratio of being employed in horizontally matching job (by the opinion of the graduate) Hungarian Graduate Career Tracking System (2007-2011) Objective heterogeneity of educational input Highest values of adjusted standardized residuals from distribution by field of study (= higher homogeneity) European Social Survey Round 2-4 (2004-2008) Subjective level of post-job entry educational requirement Means of required time of learning for someone with right qualification to complete the job (by the respondent) European Social Survey Round 2,5 (2004, 2010)

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) Match and educational homogeneity Highest value of Adj. S. Resid. Mismatch and educational homogeneity

14. Objective measurement of heterogeneity of educational input Highest value of the adjusted standardized residuals, based on the proportions taken from an occupation (ISCO 88 – 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. Objective educational heterogeneity and subjective skill requirements of graduate occupations (ISCO 88 2 digit codes) High level skill requirements and educational heterogeneity

17. Lessons and limitations In Hungary for early career graduates: educational input is more homogeneous for professionals and particularly heterogeneous for managers subjective horizontal match is stronger for professionals homogeneous educational input and higher horizontal match is combined for 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.