PRIME Summer School_ Madrid_DPalomares

8/4/2019 PRIME Summer School_ Madrid_DPalomares

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INSTITUTE OF INNOVATION AND KNOWLEDGE MANAGEMENT – INGENIO (CSIC-UPV)

DAVINIA PALOMARES-MONTERO([email protected]

)ADELA GARCÍA-ARACIL([email protected]

)

http://www.ingenio.upv.es

PRIME Summer School onThe current challenges of European Higher Education.

Trends towards the University of the Future15th to 18th September 08, Madrid, Spain

TRADE-OFFS OF SPANISH PUBLIC UNIVERSITIESMISSIONS: TEACHING, RESEARCH AND “THIRD

MISSION”



I N S T I T U T O D E G E S T I Ó N D E L A

I N N O V A C I Ó N Y D E L C O N O C I M I E N T O

2

SPANISH HIGHER EDUCATION SYSTEM.SPANISH HIGHER EDUCATION SYSTEM. Year foundation.Year foundation.



I N S T I T U T O D E G E S T I Ó N D E L A

I N N O V A C I Ó N Y D E L C O N O C I M I E N T O

3

SPANISH HIGHER EDUCATION SYSTEM.SPANISH HIGHER EDUCATION SYSTEM. Trends of enrolled students.Trends of enrolled students.

0

250000

500000

750000

1000000

1250000

1500000

1750000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Public Universities Private Universities All Universities



I N S T I T U T

O D E G E S T I Ó N D E L A

I N N O V A C I Ó N Y D E L C

O N O C I M I E N T O

4

-10%

0%

10%

20%

30%

40%

50%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Students Academic Tech&Adm

SPANISH HIGHER EDUCATION SYSTEM.SPANISH HIGHER EDUCATION SYSTEM. Variation of enrolled students and staff.Variation of enrolled students and staff.



I N S T I T U T




5

SPANISH HIGHER EDUCATION SYSTEM.SPANISH HIGHER EDUCATION SYSTEM. Evolution of publications andEvolution of publications andacademic staff.academic staff.

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

0,00

0,05

0,10

0,15

0,20

0,25

0,30

Academic staff Publications Ratio



I N S T I T U T




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RESEARCH QUESTIONRESEARCH QUESTION

1. To assess the productivity and trade-off betweenteaching, research and knowledge transfer in Spanish

public universities from 2002 to 2004.

2. To analyze teaching, research and knowledgetransfer quality for showing how Spanish HigherEducation System is developing their missions.



I N S T I T U T




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1. PRODUCTIVITY ANALYSIS1. PRODUCTIVITY ANALYSIS

• Malmquist output-oriented productivity index to decompose the totalproductivity change into:

• technological (or technical) change

• technical efficiency change (which is formed by “pure” efficiencychange and “scale” efficiency change).

• We assume constant returns-to-scale to start with.

• Annual observations of 47 Spanish public universities (DMUs areuniversities).

• Studied period: 2002-2004.

METHODOLOGY

SOURCE OF DATA

• The data set used in productivity analysis was collected as part of theAQUAMETH project supported by the 6th FP.



I N S T I T U T




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• 3 OUTPUTS

- Graduate students (proxy to measure education)- Publications (proxy to measure research)- Applied research (€) (proxy to measure knowledge transfer)

• 3 INPUTS

– Total expenditure (€)

– Academic staff

– Technical & administrative staff

• 3 MODELS – “Teaching-model” (only graduates are included)

– “Research-model” (only publications are included)

– “Knowledge transfer-model” (only applied research is included)


VARIABLES



I N S T I T U T




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RESULTS (A)

Malmquist index productivity by year, 2002-2004.

3.220.8-6.6-8.5-14.53.6-4.00.77.27.9-5.6-4.1-0.9-0.7-1.6All

years

3.323.5-9.4-7.7-16.3-3.4-9.6-1.18.06.8-15.0-38.112.821.737.32004

12.933.3-6.7-9.3-15.39.3-4.04.78.913.9-6.06.5-6.2-6.0-11.82003

-5.76.9-3.8-8.4-11.85.21.9-1.34.63.35.133.6-8.0-14.4-21.32002

MPSPTEMPSPTEMPSPTE

Knowledge Transfer ModelResearch ModelTeaching ModelYear /index

M = productivity change over the period

E = technical efficiency change (catching-up)

PT = “pure” technical efficiency

S = scale efficiency

P = technological change (frontier shift)



I N S T I T U T




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RESULTS (B)

Malmquist indexproductivity byuniversity,

2002-2004.



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2. QUALITY ANALYSIS2. QUALITY ANALYSIS

SOURCE OF DATA

• REFLEX: a major representative survey comparing the study

programme of European higher education graduates. It was fundedby the 6th FP.

• 15 countries: Austria, Finland, France, Germany, Italy, theNetherlands, Norway, Spain and the UK plus Belgium-Flanders,Czech Republic, Portugal, Switzerland, Japan and Estonia .

• Carried out in 2005 (graduates surveyed 5 years after graduation)

• Almost 40,000 answers (3,000 for each country)



I N S T I T U T




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VARIABLES

- Teaching model: To what extent has your programme been a good basis for …?(from (1) “Not at all” to (5) “to a very high extent” )

- Research model: Do you play a role in introducing innovations in your

organization? (scale (1) “Yes” to (2) “No” )

- Knowledge Transfer model: To what extent do some statements apply to your

professional role?(from (1) “Not at all” to (5) “to a very high extent” )

- Gender, Disciplines, Average grade, Description of the programme, Modes of

teaching, Part-time students, Internships and work experience during higher

education, Others type of study, Occupations, Competencies, Firm sector, Extensionof innovation in organizations, Forefront or adopting innovation, Type of contract,

Scope of firms, Responsibilities.

• DEPENDENT VARIABLES

• INDEPENDENT VARIABLES

• We have applied (ordered) probit regression.

METHODOLOGY



I N S T I T U T




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Has been the programme good basis for…RESULTS (A)



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RESULTS (B)

Do you play a role in introducing innovations in your organization?



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RESULTS (C)

To what extent do the next statements apply to your professional role?



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CONCLUSIONSCONCLUSIONS

- There are differences between models:

• Teaching model: decrease in technical efficiency and technological change.

• Research model: efficiency improvements.• Knowledge transfer model: technological progress.

- Most productivity growth was associated with improvements in research andknowledge transfer than teaching.

- Programmes in engineering and health, academically prestigious and oriented topractical knowledge are better for starting work, for further learning on the job.

- Graduates who have additional studies, who have a professional job in a privatesector and who are creative are introducing more innovations in their organizationsthan those who do not.

- Graduates who have entrepreneurial skills and who have a permanent contract play a

role in transferring knowledge from universities to enterprises.

QUALITY ANALYSIS

PRODUCTIVITY ANALYSIS



I N S T I T U T




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FURTHER RESEARCHFURTHER RESEARCH

- Additional specifications of university productivity should be examined: size, age,knowledge area, regional differences, comparison between public and privatesuniversities, etc…

- It would be interesting to apply a conditional robust nonparametric approach to domore detail interpretations

- To improve the quality analysis considering others independent variables and

comparing the results at European level.

SUGGESTIONS AND COMMENTS ARE WELCOME !



www.ingenio.upv.es

INSTITUTE OF INNOVATION AND KNOWLEDGE MANAGEMENT – INGENIO (CSIC-UPV)

Thank you very much for your attention

PRIME Summer School_ Madrid_DPalomares

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