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Do we see economies of scale in
universities? (or: differentiate, not merge at all cost)
Andrea Bonaccorsi, University of Pisa
Cinzia Daraio, University of Pisa
Léopold Simar, Institute of Statistics, UCL
Tarmo Raty, VATT Finland
Outline
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•
•
•
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Introduction
Background
Data
Methodology
Preliminary results
Further developments
Economies of scale
Key issue
– Widespread belief among policy makers that increasing returns and
critical mass effects are at place in universities
– Large debate on assumed European “fragmentation” in the university
landscape
– Arguments: (a) economies of scale (b) economies of variety
(Jacob)
However, empirical evidence is ambiguous
– Brinkman (1981), Brinkman and Leslie (1986), Cohn et al. (1989), de Groot, McMahon and
Volkwein (1991), Nelson and Hevert (1992) and Lloyd, Morgan and Williams (1993)
– Verry and Layard (1975), Verry and Davies (1976) and Adams and Griliches (1998)
– Narin and Hamilton (1996), Abbott, M., & Doucouliagos, C. (2003)
– Bonaccorsi and Daraio (2004,2005), Bonaccorsi, Daraio and Simar (2006, 2007)
Important practical policy implications
• aggregation of universities (e.g. Australian government in the ‘90s; current
debate in UK and other EU countries on critical mass);
• aggregation of institutes in large public research organisations (e.g. CNRS
in France, CNR in Italy).
Background
• Most empirical investigations done on a country base, at
university level or on specific (but limited) subjects
(e.g. Brinkman, P.T,. & Leslie, L.L. (1986), Athanassopoulos, A.D., & Shale, E. (1997),
Beasley, J.E. (1990, 1995), Flegg, A.T., Allen, D.O., Field, K., & Thurlow, T.W. (2004),
Fandel, G. (2007) )
• Lack of systematic comparisons across countries at
discipline level:
– Microdata not easily available
– Comparability issues are important
(Bonaccorsi, Daraio, Lepori,
Slipersaeter, 2007)
• Multi-output production should be taken into account
explicitly
• Any sensible efficiency analysis should take into account
the discipline-wise structure
Data and empirical background
Data
aqua
M E T H
Aquameth coverage July 2007
Reasonable sample, July 2007
aqua
Data
METH
Aquameth coverage November 2007, including France (487 universities)
NUMBER OF UNIVERSIT.
COUNTRY
Last year availab.
FINLAND
20
FRANCE
93
GERMANY
72
HUNGARY
16
ITALY
79
NETHERLAND
13
NORWAY
4
PORTUGAL
14
SPAIN
48
SWITZERLAND
12
UNITED KINGDOM
116
Methodology: Robust Nonparametric efficiency
analysis
Advantages of Robust nonparametric techniques vs.
conventional production function
– No need for functional specification
– No assumptions on the elasticity of
substitution between inputs
– Capture local effects as opposed to
estimation of average tendency
– Inclusion of external factors in a general way
Introducing conditional efficiency:
an illustration
Qzm =
Ratio between
Conditional and
Unconditional
efficiency
Region of decreasing pattern of ratios: Z has a
negative influence
If the ratios =1 then
Z has no influence on
the efficiency
1
Region of increasing pattern of ratios: Z has a
positive influence
external factor Z
Empirical analysis
• 4 countries offer data by discipline: Finland, Italy, Norway
and Switzerland (later UK, now also Netherlands and a
subsample of Germany)
• Limited time span: preliminary analysis on the year 2002
(sensitivity analysis)
• Outputs: Number of enrolled students; Number of
graduates; Number of publications
• First take a look at simple output to input ratios and how
they vary
– Scatter plots of two ratios show whether they are correlated
and there are country-wise patterns
• Conjoint production model to measure the impact of the
university size on teaching and research efficiency.
Engineering and Technology
Engineering and Technology Field
Publications
•In Italian Engineering
schools publication and
graduate intensities go
hand in hand.
1,2
Finland
Italy
Norway
Switzerland
1
0,8
•In other countries the
relation appears
opposite, but the range
in graduates is too small
and single units
dominate the view
0,6
0,4
0,2
0
0
1
2
3
4
5
6
Graduates
•Publications/academic staff
•Graduates/academic staff
In Italy also contracted employees are counted
7
•If the “outliers” are
removed, the figure is
quite unique.
Medical sciences
•Graduation has similar
patterns in Finland,
Switzerland and Norway
• Research and
education seems to go
hand in hand
Publications/academic staff
Graduates/academic staff
Natural Sciences
• No country-wise pattern
• Performance gains in
joint research and
teaching are weak
• No clear picture of the
overall relation
Publications/academic staff
Graduates/academic staff
SocHum
•Publication rate can be
at any level, regardless of
the student population
• Independent of the
country
•ISI cover just a small
portion of the peer
reviewed literature in this
field.
Preliminary Comments
• Importance of systematic international analysis
discipline-wise: subject mix matters
• The usefulness of robust conditional measures to
summarize overall effects
• Too early to draw any policy conclusions
• Next Steps
– UK universities, first estimates and then NL and a
subsample of G
Engineering and Technology – Whole
sample (2002)
Descriptive S tatisti cs
N
ACTOT
151
M inimum
8
M aximum
3856
M ean
228,84
Std. Deviat ion
388,844
ENR
151
10
38842
3757,01
4973,711
GRA
151
7
8250
1119,62
1007,630
TSTAFF
151
248
10249
2235,68
1819,737
PUB
151
0
904
102,84
136,407
PUB_AC
151
,00
5,03
,6091
,67971
GRA_AC
151
,08
68,45
10,3472
11,97422
Valid N (listwise)
151
Social Science and Humanities –Whole
sample (2002)
Descriptive Statistics
N
ACTOT
176
Minimum
11
Maximum
2210
Mean
421
Std. Deviation
324
ENR
176
214
89092
10265
11015
GRA
176
9
9654
903
1330
TSTAFF
176
48
10249
2057
1729
PUB
176
0
834
69
111
PUB_AC
176
0
3.301
0.168
0
GRA_AC
176
0.03
16
3
3
Valid N (listwise)
176
Economies of scale ENGTECH
3
2.5
But still most universities are
in the region of flat conditional
efficiency
Qzm
2
Overall positive effect of scale (number of
students)
1.5
1
Some weak evidence of
diminishing returns is also
present
0.5
Mod Conj. PUB TEACH
0.5
1
1.5
ENR ENGTECH
2
2.5
4
Economies of scale ENGTECH
4
3.5
3
Qzm
2.5
2
1.5
1
0.5
1000
2000
3000
4000
5000
6000
7000
8000
9000 10000
values of Z
Mod Conj. PUB TEACH
TSTAFF UNI
Economies of scale ENGTECH
- UK
3.5
3
No evidence whatsoever of
scale economies
Qzm
2.5
2
1.5
1
0.5
500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
values of Z
Mod Conj. PUB TEACH
ENR
Economies of scale ENGTECH
- UK
3.5
3
Qzm
2.5
2
1.5
1
0.5
1000
2000
3000
4000
5000
6000
values of Z
Mod Conj. PUB TEACH
TSTAFF UNI
Economies of scale ENGTECH
- IT
1.7
1.6
1.5
Inverted U-shaped relation
1.4
Qzm
1.3
1.2
1.1
1
0.9
0.8
0.5
1
1.5
2
values of Z
Mod Conj. PUB TEACH
4
x 10
ENR
Economies of scale ENGTECH
- IT
Effect of Z on Order-m frontier
1.6
1.4
Qzm
1.2
1
0.8
0.6
0.4
1000
2000
3000
4000
5000
6000
7000
8000
9000 10000
values of Z
Mod Conj. PUB TEACH
TSTAFF UNI
Economies of scale SOCHUM
3
2.5
Very small Faculties are sub-optimal
Qzm
2
1.5
1
0.5
0.5
1
1.5
2
2.5
3
3.5
4
values of Z
Mod Conj. PUB TEACH
4.5
4
x 10
ENR
Economies of scale SOCHUM
4
3.5
3
Qzm
2.5
2
1.5
1
0.5
1000
2000
3000
4000
5000
6000
7000
8000
9000 10000
values of Z
Mod Conj. PUB TEACH
TSTAFF UNI
Economies of scale SOCHUM UK
1.6
1.4
Qzm
1.2
1
0.8
0.6
0.4
0.2
2000
4000
6000
8000
10000
12000
14000
values of Z
Mod Conj. PUB TEACH
ENR
16000
Economies of scale SOCHUM UK
3
2.5
Qzm
2
1.5
1
0.5
1000
2000
3000
4000
5000
6000
values of Z
Mod Conj. PUB TEACH
TSTAFF UNI
Economies of scale SOCHUM IT
2
1.8
Qzm
1.6
1.4
1.2
1
0.8
1
2
3
4
5
6
7
values of Z
Mod Conj. PUB TEACH
8
4
x 10
ENR
Economies of scale SOCHUM IT
2
Qzm
1.5
1
0.5
1000
2000
3000
4000
5000
6000
7000
8000
9000 10000
values of Z
Mod Conj. PUB TEACH
TSTAFF UNI
Conclusions on scale and efficiency
• Economies of scale should not be examined at the level of
universities at aggregate level
• Differentiated pattern by discipline
• Also some country-level differences emerge
No empirical support for a generalized policy of pressure on
universities to grow or merge
Rather, each scientific/ educational field must find its own “optimal”
scale
Policies of concentration/ merger should be aimed at helping
universities to find their own optimal configuration among
disciplines, each of which follows differentiated patterns
University as a strategic multi-divisional agent
The key to strategic behaviour is differentiation
Differentiation of European universities in PhD
education
• PhD education crucial in knowledge society
• Internationalization and mobility
• Competition
• Institutional adaptation
• Differentiation and “division of academic labor” as response to
enlargement of the market and competition
Variable observed
Number of graduate students/ Number of undergraduate students
(ratio)
Institutional differentiation
Entropy measure
h(pi) = log (1/ pi)
H=
n
 pi log (1/ pi)
Weitzmann’s diversity
V(Z) = max ( V(Z\x) + d (Z\x, x))
x Z
Mean sum of squared distance
SSD =
n

i=1
n

j=1
(wi – wj/ w^)2
n
n
MSSD = 1/n2   (wi – wj/ w^)2
i=1 j=1
PhD intensity. Netherlands. Year 1994-2004
0,05
0,045
PhD intensity
0,04
0,035
0,03
0,025
0,02
0,015
0,01
0,005
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Differentiation in PhD intensity. Netherlands.
Year 1994-2004
160
Differentiation index
140
120
100
80
60
40
20
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
PhD intensity. Finland. Year 1994-2005
0,135
PhD intensity
0,13
0,125
0,12
0,115
0,11
0,105
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Differentiation in PhD intensity. Finland.
Year 1994-2005
Differentiation index
160
140
120
100
80
60
40
20
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
PhD intensity. Switzerland. Year 1994-2003
0,18
0,175
Phd intensity
0,17
0,165
0,16
0,155
0,15
0,145
0,14
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Differentiation in PhD intensity. Switzerland.
Year 1994-2003
80
70
Differentiation index
60
50
40
30
20
10
0
1994 1995
1996 1997
1998 1999 2000
2001 2002 2003
PhD intensity. United Kingdom. Year 1996-2003
0,25
PhD intensity
0,2
0,15
0,1
0,05
0
1996
1997
1998
1999
2000
2001
2002
2003
Differentiation in PhDintensity. United Kingdom.
Year 1996-2003
10000
9000
8000
Differentiation index
7000
6000
5000
4000
3000
2000
1000
0
1996
1997
1998
1999
2000
2001
2002
2003
PhD intensity. Spain. Year 1994-2002
0,06
PhD intensity
0,05
0,04
0,03
0,02
0,01
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
Differentiation in PhD intensity. Spain.
Year 1994-2002
1400
Differentiation index
1200
1000
800
600
400
200
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
PhD intensity. Italy. Year 2001-2005
0,02
0,018
0,016
PhD intensity
0,014
0,012
0,01
0,008
0,006
0,004
0,002
0
2001
2002
2003
2004
2005
Differentiation in PhD intensity. Italy.
Year 2001-2005
3500
Differentiation index
3000
2500
2000
1500
1000
500
0
2001
2002
2003
2004
2005
Comparative analysis of PhD intensity
0,25
PhD intensity
0,20
Switzerland
Netherlands
Finland
Spain
Italy
United Kingd
0,15
0,10
0,05
0,00
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Comparative analysis of differentiation in PhD intensity
Normalized differentiation index
0,900
0,800
0,700
0,600
0,500
0,400
0,300
0,200
0,100
0,000
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Switzerland
Netherlands
Finland
Spain
Italy
United Kingdom
Conclusions
• Universities must learn to compete in an international
environment
• To compete, you need a strategy
• The name of the game is strategic differentiation
• by scale and scope
• by subject mix
• by main type of education (undergraduate, professional
master, research training)
• by ambition in research (regional producer of usable
knowledge; average research producer; world class research
university)
• by interactions with stakeholders (proximity vs international;
industry vs territory/society)
• by funding mix
• To have a strategy you need indicators of positioning and
of competitive dynamics (not only rankings)