Jean Monnet Programme - European Module
Jean Monnet Studies on Economic Integration: within and beyond Europe
Decision Nr 2011-3263/001-001
Methodology of European Studies
This project has been funded with support from the European Commission. This publication reflects the views only of the author, and the Commission cannot
be held responsible for any use which may be made of the information contained therein.
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EFFICIENCY OF EUROPEAN PUBLIC
HIGHER EDUCATION INSTITUTIONS:
A TWO-STAGE MULTICOUNTRY
MULTIPERIOD APPROACH
Wolszczak-Derlacz J. and Parteka A.
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 MOTIVATION
 LITERATURE REVIEW
 METHODOLOGY
University production function
Data Envelopment Analysis (DEA)
Two-stage bootstrap DEA analysis
Data and panel composition
 ESTIMATION
Key characteristics of HEIs
Step1: Non-parametric efficiency measure
Step 2: parametric approach: estimated model
Robustness check
 CONCLUSION
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MOTIVATION
 Academic research - a key factor of human capital creation
 Lisbon Strategy/Europe 2020
The debate about universities in Europe: their role
(teaching versus research), funding, governance and
efficiency
“In the global knowledge economy, people’s skills, learning, talents, and
attributes - their human capital - have become key to both their ability to earn a
living and to wider economic growth. Education system can do much to help
people to realize their potential, but when they fail it can lead to lifelong social
and economic problems”(OECD, 2007 p.21)
“...Europe simply must have a first-class university system - with universities
recognized internationally as the best in the various field involved” (European
Commission, 2003 p.22)
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LITERATURE REVIEW
 DEA still not popular in HEIs (bibliographic database of DEA
articles published in scientific journals in the years 1950 – 2007,
maintained by Gattoufi et al. (2010), records only about 3.5% of papers
dedicated to the HE);
 HEIs: mainly country-specific studies (Australia: Abbott and
Doucouliagos, 2003; Worthington and Lee, 2008; UK: Flegg et al., 2004;
Johnes 2006; Italy: Abramo et al., 2008; Agasisti and Salerno, 2007;
Bonaccorsi et al., 2006; Tommaso and Bianco, 2006; Austria: Leitner et
al., 2007;Germany: Fandel, 2007; Kempkes and Pohl, 2006);
 Cross-country studie: Bonaccorsi et.al., 2007; Agasisti and
Johnes, 2009.
 Second step: mainly tobit model used: Kirjavainen and
Loikkanen, 1998; Kempkes and Pohl, 2008. Bootstrapping
procedure: Oliviera and Santos, 2005; Alexander et al., 2010
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University production function
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Research output
How to measure research output:
 expert peer-review process
 evaluation of research orientation e.g PhD to total
number of students
 comparision of research grants
 bibliometric indicators
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Bibliometric indicators
Advantages:
 comparable across universities
 easy to implement
 low cost
 quick update
Disadvantages
 quality versus quantity
 differences across disciplines
 self citation
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Data envelopment analysis (DEA)
 no functional form
 multiply inputs and multiply outputs
 relative efficiency of each HEI (vis-a-vis the best
performence)
 output orientated model: maximising output while
using no more than observed amount of inputs
 3 inputs: academic staff, students, total revenues in
euro; 2 outputs: graduates and scientific publications
listed in ISI Web of Knowledge
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TWO-STAGE BOOTSTRAP DEA ANALYSIS
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BOOTSRAPPED TRUNCATED REGRESSION
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DATA AND PANEL COMPOSITION
Micro database on 259 European public HEIs, 2001-2005
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Key characteristics of HEIs
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STEP1: NON-PARAMETRIC - RESULTS OF THE
EMPIRICAL ANALYSIS OF EFFICIENCY PERFORMENCE
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THE DISTRIBUTION OF EFFICIENCY SCORES
BY COUNTRY (ALL YEAR POOLED)
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STEP 2: PARAMETRIC APPROACH: ESTIMATED MODEL
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RESULTS
TABLE: The determinants of inefficiency scores (truncated
regression) - DEA 3 input 2 output model
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ROBUSTNESS CHECK
 2 input 2 output DEA model
 Double bootstrap procedure
 Alternative truncation points
 Articles only as a research output
 Change in the number of bootstrap
replications: L=500, L=2000
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DEA 2 INPUT 2 OUTPUT MODEL
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DOUBLE BOOTSTRAP PROCEDURE
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ALTERNATIVE TRUNCATION POINTS
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Conclusion
 Creation of an unique micro database - need to expand
country coverage
 High variability of efficiency within and between countries:
difficult to point out the best country
 Inefficiency is determined by:
- size (number of students and number of different
faculties)
- specialisation (number and composition of faculties)
- tradition (year of foundation)
- gender structure of the academic staff
- the share of revenues from core funding (public
sources)
 Clear political implications, especially for those who manage
individual HEIs
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Thank you for your attention
[email protected]
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