measuring graduate occupations and their skill requirements in hungary peter robert, institute for...
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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
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
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
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)
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
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)
FEOR – ISCO cross-matching ISCO 88 FEOR 08
10 Legislators, senior officials and managers11 Legislators and senior officials 11 Chief executives, senior officials and legislators12 Corporate managers 12 Administrative and commercial managers13 Managers of small enterprises 13 Production and specialised services managers
14 Managers of other economic units20 Professional
21 Phys, mathem, engin science professionals 21 Science and engineering professionals22 Life science and health professionals 22 Health professionals23 Teaching professionals 23 Social professionals24 Other professional 24 Teaching professionals
25 Business professionals26 Legal and social professionals27 Cultural, sport, arts professionals29 Other highly qualified administrators
30 Technicians and associate professionals31 Physical, engineering science associate prof 31 Science and engineering associate professionals32 Life, science and health associate prof 32 Professional managers, supervisors33 Teaching associate professionals 33 Health associate professionals34 Other associate professionals 34 Teaching associate professionals
34 Social and labour market services professionals35 Business and administration associate professionals36 Cultural, sport, arts associate professionals39 Other administrators
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)
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)
Indicators of educational heterogeneity of graduate occupations
Content Measurement Source
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)
Objective measurement of heterogeneity of educational input
Chief executives, senior officials and legislatorsAdministrative and commercial managers
Production and specialised services managersManagers of other economic units
Science and engineering professionalsHealth professionalsSocial professionals
Teaching professionalsBusiness professionals
Legal and social professionalsCultural, sport, arts professionals
Other highly qualified administratorsScience and engineering associate professionals
Professional managers, supervisorsHealth associate professionals
Social and labour market services professionalsBusiness and administration associate professionals
Cultural, sport, arts associate professionalsOther administrators
0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.0021,2 (administration, law enforcement and
military)8,4 (economics)
7,8 (agriculture)
12,4 (economics)
48,0 (informatics)94,7 (medical and health
sciences)42,2 (social sciences)
53,4 (teachers training)
42,0 (economics)
51,4 (law)
19,6 (social sciences)
4,9 (natural sciences)
14,9 (engineering)
3,2 (humanities)30,2 (medical and health
sciences)13,4 (teachers training)
28,6 (economics)
15,6 (sport sciences)
2,6 (economics)
FEOR/ISCO 1
FEOR/ISCO 2
FEOR/ISCO 3
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
Subjective measurement of horizontal matching Ratio of subjective horizontal match in graduate occupations by 2 digit FEOR 08 categories N=13,147
Chief executives, senior officials and legislators
Administrative and commercial managers
Production and specialised services managers
Managers of other economic units
Science and engineering professionals
Health professionals
Social professionals
Teaching professionals
Business professionals
Legal and social professionals
Cultural, sport, arts professionals
Other highly qualified administrators
Science and engineering associate professionals
Professional managers, supervisors
Health associate professionals
Social and labour market services professionals
Business and administration associate professionals
Cultural, sport, arts associate professionals
Total
.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%
79.5%
77.1%
82.0%
82.4%
89.4%
96.4%
93.8%
94.9%
81.6%
90.4%
79.2%
74.9%
65.2%
63.8%
73.7%
84.7%
67.8%
62.0%
83.5%
FEOR/ISCO 1
FEOR/ISCO 2
FEOR/ISCO 3
Subjective horizontal match and objective educational heterogeneity of graduate occupations (FEOR 2 digit codes)
Match and educational homogeneity
Highest value of Adj. S. Resid.
60.0% 95.0%
-15.00
20.00
55.00
90.00
Chief executives, senior of-ficials and legislators
Administrative and commercial managers
Production and specialised services managers
Managers of other economic units
Science and engineering professionals
Health professionals
Social professionals
Teaching professionals
Business professionals
Legal and social professionals
Cultural, sport, arts pro-fessionals
Other highly qualified
administrators
Science and engineering assoc. pro-
fessionals
Professional managers, supervisors
Health associate professionals
Social and labourmarket services professionals
Business and administration assoc. professionals
Cultural, sport, arts assoc.
professionals
Rate of horizontal match
Mismatch and educational heterogeneity
Match and educational heterogeneity
FEOR/ISCO 1
FEOR/ISCO 2
FEOR/ISCO 3
Mismatch and educational homogeneity
Objective measurement of heterogeneity of educational inputHighest 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
Legislators, senior officials and managers
Corporate managers
Managers of small enterprises
Phys, mathem, engin science professionals
Life science and health professionals
Teaching professionals
Other professional
Physical,engineering science associate prof
Life,science and health associate prof
Teaching associate professionals
Other associate professionals
0 20 40 60 80 100 120 140
26,3 (Public order and safety)
20,3(Economics/commerce/business
administration)36,6 (General/
no specific field)
61,5 (Technical and engineering)
95,5 (Medical/health services/ nursing )114,9
(Teacher training/ education)
46,1 (Law and legal services)
60,6 (Technical and engineering) 118,4
(Medical/health services/ nursing)
53,6 (Teacher training/ education)
52,3(Economics/commerce/business
administration)
FEOR/ISCO 1
FEOR/ISCO 2
FEOR/ISCO 3
Subjective skill level requirements for graduate occupationsMeans 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
Legislators, senior officials and managers
Legislators and senior officials
Corporate managers
Managers of small enterprises
Professional
Phys, mathem, engin science professionals
Life science and health professionals
Teaching professionals
Other professional
Physical,engineering science associate prof
Life,science and health associate prof
Teaching associate professionals
Other associate professionals
Total
0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00
469.06
579.36
536.02
436.60
493.42
515.62
499.83
530.24
422.88
446.99
337.15
310.52
347.81
441.48
FEOR/ISCO 1
FEOR/ISCO 2
FEOR/ISCO 3
Objective educational heterogeneity and subjective skill requirements of graduate occupations (ISCO 88 2 digit codes)
300.00 400.00 500.00 600.00
10
50
90
Legislators, senior of-ficials and managers
Corporate managers
Managers of small enterprises
Phys, mathem, engin science professionals
Life science and health professionals
Teaching professionals
Other professional
Physical,engineering science associate prof
Life,science and health associate prof
Teaching associate pro-fessionals Other associate pro-
fessionals
Low level skill requirements and educational homogeneity
Highest value of Adj. S. Resid.
Level of post-job entry requirements
High level skill requirements and educational homogeneity
Low level skill requirements and educational heterogeneity
FEOR/ISCO 1
FEOR/ISCO 2
FEOR/ISCO 3
High level skill requirements and educational heterogeneity
Lessons and limitationsIn 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
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
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.