Higher Education
Some International comparisons
Domingo Docampo
Universidade de Vigo (Spain)
On sabbatical at ECE-UNM
Outline of the Talk

World Demand of Higher Education
 The case of Australia
 Two models of Higher Education Funding
 OECD Indicators for the two models
 How to tell the models apart?
 ARWU data on research
 Comparative performance of countries and US regions
 Two conclusions
World’s demand of HE


Enrolment in Higher Education

97M students in 2000

263M in 2025 (predicted)
Mobility in Higher Education

1.9M foreign in 2000 (2%)

7.2M in 2025 (3%)
World’s share of international
students (2000-05)
ITA (115)
SPA (116)
BEL (81)
NZE (600)
JAP (125)
USA (85)
CAN (82)
AUS (112)
UK (95)
FRA (123)
GER (98)
Mobility from Asia (1 million)
KOR (1)
NZE (3)
FRA (4)
CAN (5)
GER (9)
USA (36)
JAP (11)
AUS (13)
UK (14)
Asian mobility relative to GDP
KOR (1)
FRA (2)
JAP (3)
USA (3)
GER (4)
CAN (6)
NZE (38)
UK (8)
AUS (22)
Mobility to Australia
INTERNATIONAL STUDENTS IN AUSTRALIA
200000
06-07
05-06
180000
04-05
03-04
160000
02-03
140000
120000
100000
01-02
80000
00-01
99-00
60000
40000
20000
0
96-97
95-96
97-98
What happened in Australia?
 Policy Reforms in 1987
 Income-contingent loans
 Government change in 1996
 New Higher Education Act 2003
 Changes
in Tuition
 Internationalization of HE
On Tuition








If tuition was the answer, then what was the question?
Governments felt financially pressured, began to
question whether higher education is a public good?
Private benefits do accrue to graduates.
Positive externalities: Good citizens, Good taxpayers.
Debate in Australia 1986
New Zealand followed suit
UK in 2003
Taboo in Continental Europe
The case for and against
Higher Education as a public good

Education is a basic right

Graduates will return the benefits by paying more
taxes (around US$ 200,000 during a lifetime)

Income tax is paid by many more non-graduates than
graduates: free higher education is horizontally
inequitably

The taxpayer gets a good deal is a dangerous argument
(R&D expenses)
Two models
 Anglo-American
model


Encourages Diversity
Heterogeneous Institutions
 Quality comparisons
 Scandinavian
model
All programs ‘are’ equal
Homogeneous Institutions
 Quality of a Public Service


Two approaches to HE Funding

Utopian
 Very high taxes
 R&D commitment
 High Public Spending
 High Enrolment

Practical
 Much lower taxes
 R&D commitment
 High Private Spending
 High Enrolment
Are there utopian countries?
 Is
there a way to tell a country apart?
 Shouldn’t it be obvious?
 Rationalize the obvious using
 OECD
data
 OECD indicators
 The Economist and World Bank Indicators
Set of Indicators

Taxes on Average worker (I5)
 Enrolment (I6)
 Percentage of GDP of:
 Public expenditure on Education (I1)
 Public expenditure on HE (I2)
 Private expenditure on HE (I3)
 Total spending on HE (I4)
 Gross domestic expenditure on R&D (I7)
Main data Table
Data
Country
Australia
Canada
Denmark
Finland
France
Germany
Italy
Japan
Korea
Netherlands
Norway
New Zealand
Spain
Sweden
United Kingdom
United States
OECD average
Pub Edu
I1
4.8
5.0
8.3
6.5
5.9
4.7
4.9
3.7
4.6
5.1
7.6
6.8
4.3
7.5
5.4
5.7
5.5
Pub HE
I2
1.1
1.7
2.5
2.1
1.2
1.2
0.8
0.6
0.6
1.3
2.3
1.6
1.0
2.2
1.1
1.5
1.3
Priv HE Total HE
I3
I4
0.8
1.9
1.0
2.7
0.1
2.6
0.1
2.1
0.2
1.4
0.1
1.3
0.2
1.0
0.8
1.4
2.0
2.6
0.3
1.6
0.1
2.4
0.6
2.2
0.3
1.2
0.2
2.3
0.3
1.4
1.6
3.1
0.4
1.7
Taxes
I5
28.6
32.3
41.5
43.8
47.4
50.7
45.7
26.6
16.6
43.6
36.9
20.7
38.0
48.0
31.2
29.6
36.5
Enrolment
I6
73.0
58.0
67.0
88.0
56.0
51.0
57.0
51.0
85.0
58.0
81.0
77.0
62.0
83.0
64.0
83.0
63.0
R&D
I7
1.7
2.0
2.6
3.5
2.2
2.5
1.2
3.2
2.6
1.8
1.8
1.2
1.1
4.0
1.9
2.7
2.3
Correlation Matrix
Correlation Pub Edu Pub HE Priv HE Total HE Taxes Enrolment R&D
Pub Edu
Pub HE
Priv HE
Total HE
Taxes
Enrolment
R&D
1.00
0.90
1.00
-0.40
-0.41
1.00
0.48
0.56
0.53
1.00
0.25
0.32
-0.75
-0.39
1.00
0.52
0.45
0.29
0.69
-0.32
1.00
0.24
0.29
0.08
0.34
0.19
0.19
1.00
Total vs. Public Expenditures
FIGURE 2:TOTAL EXPENDITURES vs PUBLIC
EXPENDITURES IN HIGHER EDUCATION
PUBLIC EXPENDITURES
3.0
2.5
Correlation=0.56
FIN
2.0
DEN
NOR
SWE
NZE
1.5
NET
GER FRA
UK
SPA
ITA
JAP
1.0
0.5
CAN
USA
AUS
KOR
0.0
0.0
0.5
1.0
1.5
2.0
TOTAL EXPENDITURES
2.5
3.0
3.5
TAXES vs PRIVATE EXPENDITURES
FIGURE 4: TAXES ON AVERAGE WORKER vs PRIVATE
EXPENDITURES IN HE
TOTAL EXPENDITURES HE
KOR
USA
correlation=0.75
CAN
NZE
JAP
AUS
UK
NET
SPA
ITAFRA
NOR DEN
SWE
FIN
TAXES ON AVERAGE WORKER
GER
Total Expenditures in HE
vs. Enrolment
FIGURE 7: TOTAL EXPENDITURES IN HE vs ENROLMENT
FIN SWE
KOR
NOR
NZE
ENROLMENT
AUS
SPA
ITA
GER
UK
DEN
FRA NET
JAP
correlation=0.69
TOTAL EXPENDITURES IN HIGHER EDUCATION
CAN
USA
PRINCIPAL COMPONENTS
FIRST PRINCIPAL COMPONENT
FACTOR ANALYSIS
DEN
NOR
SWE
FIN
NZE
USA
CAN
AUS
FRA NET
GER
UK
SPA
ITA
JAP
SECOND PRINCIPAL COMPONENT
KOR
PRINCIPAL COMPONENT 1
FIRST PRINCIPAL COMPONENT
DEN
NOR
SWE
FIN
47% OF THE VARIANCE EXPLAINED
USA
NZE
CAN
KOR
AUS
NET FRA
UK
GER SPA
ITA
JAP
PRINCIPAL COMPONENT 2
SECOND PRINCIPAL COMPONENT
KOR
40.5% OF THE VARIANCE EXPLAINED
USA
CAN AUS JAP
NZE
UK
SPA
NET NOR ITA
FIN SWE
FRA DEN
GER
Understanding the data

Normalize indicators: best gets 100 points
 Rearrange
proportionally
 Subtract OECD average

Look at the sign of the correlation
 I1,
I2 and I5 correlate positively.
 I3 correlates negatively with them all.
 I4 and I6 correlate positively
A measure for Utopia

M1 first principal component using only I1, I2,
I3 and I5
 M2 first principal component using only I4, I6
and I7
Results of Measures M1 and M2
Country DEN SWE NOR FIN FRA GER NET ITA NZE UK SPA CAN AUS USA JAP KOR
M1 34
28
27
23
8
7
4
-2
-3
-6
-6
-10 -16 -21 -28 -49
M2 17
24
23
23
-9
-14
-5
-16 18
-5
-9
15
10
37 -13 29
The new clustering
Results of Indicators M1 and M2
40
USA
30
KOR
FIN
NZE
SWE
NOR
20
CAN
DEN
M2
AUS
Correlation = 0.09
10
0
-6 0
-5 0
-4 0
-3 0
-2 0
-1 0
0
UK
SPA
-1 0
JAP
NET
FRA
GER
ITA
-2 0
M1
10
20
30
40
LANDING FROM UTOPIA
Principal component for indicators I1,I2,I3,I5
DEN
SWE
NOR
FIN
FRA
GER
NET
ITA
NZE
UK
SPA
CAN
AUS
USA
63% of the variance explained
JAP
KOR
LANDING FROM THE FUTURE
Principal component for indicators I4,I6,I7
80
USA
60
SWE
FIN
KOR
60% of the variance explained
40
DEN
NOR
CAN
20
NZE
AUS
0
JAP
-20
-40
UK
NET
FRA
GER
SPA
ITA
-60
Quality Assessment
Shanghai Jiao Tong University’s Academic
Ranking of World Universities

Based
on Scientific Production
Sound
Indicators
Reliable
Data
Data
can be aggregated for countries

Allows international comparisons

It is not the whole story but…
World
Rank
Institution
ARWU
1
2
3
4
5
6
7
8
8
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Harvard Univ
Univ Cambridge
Stanford Univ
Univ California - Berkeley
Massachusetts Inst Tech (MIT)
California Inst Tech
Columbia Univ
Princeton Univ
Univ Chicago
Univ Oxford
Yale Univ
Cornell Univ
Univ California - San Diego
Univ California - Los Angeles
Univ Pennsylvania
Univ Wisconsin - Madison
Univ Washington - Seattle
Univ California - San Francisco
Tokyo Univ
Johns Hopkins Univ
Univ Michigan - Ann Arbor
Kyoto Univ
Imperial College London
Univ Toronto
Univ Illinois - Urbana Champaign
Score on Score on Score on Score on Score on Score on
Alumni
Award
HiCi
N&S
SCI
Size
100
96.3
39.7
70.6
72.9
57.1
78.2
61.1
72.9
62
50.3
44.9
17.1
26.4
34.2
41.5
27.7
0
34.8
49.5
41.5
38.3
20.1
27.1
40.1
100
91.5
70.7
74.5
80.6
69.1
59.4
75.3
80.2
57.9
43.6
51.3
34
32.1
34.4
35.5
31.8
36.8
14.1
27.8
0
33.4
37.4
19.3
36.6
100
53.8
88.4
70.5
66.6
59.1
56
59.6
49.9
48
59.1
56
59.6
57.6
57
53.3
53.3
55.5
41.4
40.7
61.5
36.9
40
38.5
45.5
100
59.5
70
72.2
66.4
64.5
53.6
43.5
43.7
54.3
56.6
48.4
54.8
47.5
41.7
45.1
47.6
54.8
51.5
52.2
41.6
36.2
39.7
36.5
33.6
100
67.1
71.4
71.9
62.2
50.1
69.8
47.3
54.1
66
63
65.2
65.6
77.3
73.6
68.3
75.5
61.1
85.5
68.8
76.9
72.4
64.2
78.3
57.7
73.6
66.5
65.3
53.1
53.6
100
45.8
58
41.8
46
49.3
40.1
47.1
34.9
40
29.3
27.8
48.2
35.2
25.3
31.2
31.7
40.2
44.8
26.3
Total
Score
100
73
73
72
70
66
62
59
59
58
56
54
51
50
50
49
49
48
47
47
45
44
43
43
43
CORRELATION MATRIX
correlation
ALUMNI STAFF
HiCi
S&N
Sci
SIZE
ALUMNI
1.00
0.76
0.61
0.68
0.54
0.67
STAFF
0.76
1.00
0.66
0.71
0.48
0.72
HiCi
0.61
0.66
1.00
0.86
0.69
0.73
S&N
0.68
0.71
0.86
1.00
0.71
0.80
Sci
0.54
0.48
0.69
0.71
1.00
0.62
SIZE
0.67
0.72
0.73
0.80
0.62
1.00
HOW GOOD ARE THE BETTER
RATIO avg(4Q)/avg(1Q)
120
USA
100
80
60
SWI
UK
DEN
NOR
FIN
40
JAP
CAN
SCA FRA
NET SWE AUS
GER
ITA
20
0
SPA KOR
NZE
Cutting the US in
European like slices
GDP in millions
Regions
GDP
%
MID
STH
EAS
CA
WST
NY
TX
FL
2,645
2,093
1,994
1,655
1,426
975
951
670
4.34
3.43
3.27
2.71
2.34
1.60
1.56
1.10
12,409
20.34
USA TOTAL
How good are the better now
RATIO avg(4Q)/avg(1Q)
120
CA
100
80
60
NY
40
USA
EAS
MID
20
0
UK WST
FL
TX
JAP
CAN STH SCA FRA
NET
AUS GER
ITA
SPA KOR
Compare only the best university

Given a REGION X, let N(X) be equal to
GDP(US)/GDP(X)
 Let USX be the median of the first N(X) US
universities’ rank.
 Let Lag(X) be the difference between the rank
of the best university from region X and USX.

Normalize the result
lag(X)/USX
Prima Donna (1)
LAG(X)/USX
40.0
KOR
35.0
30.0
SPA
25.0
ITA
20.0
15.0
GER
FRA
10.0
5.0
NZE
SWI USA SIN DEN ISR FIN SWE NOR
0.0
UK
-5.0
NET CAN
AUS
BEL JAP AUT
Prima Donna (2)
LAG(X)/USX
5
SCA
TX
4
AUS
3
WST
2
MID
1
USA
0
UK
-1
-2
EAS
CA
NY
NET
CAN
FL
Prima Donna (3)
LAG(X)/USX
90
CHI
80
70
60
50
40
KOR
SPA
30
ITA
20
FRA
10
0
JAP
STH
GER
Universities in ARWU
Number of Universities by share of GDP Actual number of universities
25
50
100 200 500
25
50 100 200 500
USA
5
10
20
41
102
19
37
54
87 167
1
1
3
5
14
6
10
10
12
14
CA
1
2
4
9
22
4
6
12
17
27
MID
1
1
2
4
11
5
9
14
18
38
EAS
1
2
3
8
3
5
6
8
15
NY
UK
1
2
3
6
16
3
5
11
22
43
JAP
2
3
6
13
32
2
2
6
9
32
CAN
1
2
3
9
1
2
4
8
22
1
3
5
13
1
3
6
10
24
WST
1
STH
2
3
7
17
2
3
13
32
1
TX
1
2
3
8
2
2
7
13
SCA
1
1
3
7
1
7
9
25
FRA
1
3
6
15
1
4
6
21
1
NET
1
2
5
1
2
7
12
GER
4
8
20
5
15
40
1
2
AUS
1
1
2
5
2
6
16
ITA
3
5
14
1
6
23
1
1
FL
1
2
5
1
2
4
1
Ratio actual/expected
25 50 100 200 500
374 364 265 214 164
885 738 369 221 103
369 277 277 196 125
612 551 428 275 233
751 626 375 250 188
380 317 348 348 272
124 62 93 70 99
230 230 230 230 253
156 234 234 195 187
117 87 189 187
257 128 225 167
150 525 338 375
67 133 100 140
227 397 272
126 189 202
190 285 304
37 110 168
91 91 73
HOW GOOD ARE THE BETTER?
SLOPE ACROSS ARWU
JAP FL
WEST
SIN BEL
MID ISR
EAS
NY
SWI
CA
USA DEN
SWE FIN NOR TX UK
NET CHI
CAN FRA
STH
AUS
SPA GER
ITA KOR
CLUSTERING (25-500)
Principal components clustering
(94% of the variance explained)
AUS
GER
STH
ITA FRA
SPA
FL
JAP
KOR
SCA
NET
TX
WST
CAN
UK
MID USA
EAS
NY
CA
RANKING ACROSS ARWU
ACROSS ARWU First Principal Component
(63% of the variance explained)
CA
NY
EAS
UK
SCA USA
MID
CAN NET WST
TX AUS
STH
GER
FRA JAP
ITA
FL
KOR SPA
From 50 to 500
Best 50: First Principal Component
(68% of the variance explained)
SCA
EAS NY
CA
UK
NET
USA
CAN WST
MID AUS
TX
STH
GER
FRA
JAP ITA
FL
KOR SPA
From 100 to 500
Best 100 Principal Component
(81% of the variance explained)
SCA
UK
NET
EAS
NY AUS
CAN CA
WST
USA
MID
TX GER
STH
FRA
ITA
JAP
FL
KOR SPA
Over-share of GDP (500)
Over-representation in arwu (500)
SCA
AUS
UK NET
CAN
EAS
WST GER STH
NY
ITA TX USA
FRA
MID
KOR CA JAP SPA
FL
BEST 500 (GDP SHARE)
AVERAGE RANKING OF THE BEST (GDP SHARE) UNIVERSITIES
ON ARWU 500 (FIRST TIER)
250
FRA (15)
FL (5)
200
STH (17)
GER (20)
150
NET (4) TX (8)
100
SCA (7) UK (16)
CA (14)
50
0
NY (8)
EAS (16)
WST (13)
AUS (5) CAN (9)
USA (102)
MID (22)
CORRELATION MATRIX
Correlation
ALU
STAFF
HICI
S&N
SCI
SCORE
ALU
1.00
0.96
0.68
0.80
0.50
0.90
1.00
0.67
0.79
0.44
0.89
HICI
1.00
0.96
0.74
0.91
S&N
BEST 500
BIG COUNTRIES
1.00
0.74
0.97
1.00
0.75
STAFF
SCI
SCORE
1.00
Sci (500)
SCI SCORE BEST 500 (AVERAGE SHARE GDP REGIONS)
120
100
CA CAN
UK AUS MID
NET USA NY SCA
SWE
80
STH WST ISR SWI
FL
TX KOR
EAS GER
ITA
JAP SPA
FRA
60
40
20
0
Quality vs Quantity (500)
SCI vs S&N
120
100
AUS
SCI
80
KOR
SPA
ITA
JAP
CAN
SCA NET
UK
USA
GER
FRA
60
40
CORRELATION= 0.74
20
0
0
20
40
60
S&N
80
100
120
Quality Indicators
HICI vs S&N (500) Large Countries
UK
SCA
USA
NET
CAN
AUS
S&N
GER
FRA
JAP
ITA
KOR
SPA
CORRELATION= 0.96
HiCi
Conclusions (1)
 There
are indeed two models to
properly fund Higher Education
 Choose
fullest.
one, but please, to the
Conclusions (2)
 Benchmarking
is a good basis for
improvement. Through international
benchmarking countries can identify
best practices and ways forward.
 Identify
the appropriate incentives to
encourage and reward excellence.
Conclusions (3)
 Galbraith
once said that given the choice
of proving that changes are unnecessary,
most people…
 Please,
do not.
 THANKS
HiCi (500)
HiCi SCORE BEST 500 (AVERAGE SHARE GDP REGIONS)
120
CA
100
EAS
80
NY
USA
MID
60
WST UK
TX
SWI
STH FL CAN
AUS SWE SCA
NET ISR
40
JAP GER
ITA FRA
20
SPA
KOR
0
S&N (500)
S&N SCORE BEST 500 (AVERAGE SHARE GDP REGIONS)
120
CA
100
80
NY
SWI
UK WST EAS USA
60
TX
MID NET
SCA SWE CAN ISR
40
AUS
STH
GER
FRA FL
JAP
ITA
SPA
20
0
KOR
ACROSS ARWU
AVERAGE RANKING OF THE BEST UNIVERSITIES ACCORDING TO GDP SHARE
index =5*( 25-A)+4*(50-B)+3*(100-C)+2*(200-D)+500-E
REGIONS
CA
EAS
UK
NY
USA
MID
WST
CAN
TX
NET
STH
SCA
JAP
AUS
FRA
FL
GER
25
3
1
2
7
4
8
17
24
20
50
3
3
5
7
7
13
19
24
38
40
35
48
37
51
A
B
100
4
7
12
7
13
19
24
29
38
40
51
50
64
55
68
62
65
C
200
8
17
29
10
28
37
47
48
56
54
82
56
126
70
103
111
91
D
500
58
63
76
65
104
126
113
113
121
96
168
68
290
117
212
206
156
E
INDEX
1,442
1,426
1,393
1,384
1,372
1,255
1,072
1,044
968
956
887
843
785
784
716
702
670
ACROSS ARWU (2)
NORMALIZED INDEX
120
CA
100
EAS
UK
NY
USA
MID
80
WST CAN
TX
NET
STH
60
SCA
JAP
AUS
FRA
40
20
0
FL
GER
International Students (2)
TO OECD COUNTRIES BY ORIGIN (thousands)
? (90)
2004
S Am (135)
Oceania (20)
China (330)
N Am (80)
India (120)
Africa (250)
Europe (570)
Asia (540)
International Students
BY DESTINY 2004
700000
600000
USA
500000
400000
UK
300000
200000
GER
FRA
AUS
C AN
100000
0
JAP
NZE
BEL SPA ITA SWE SWI AUT
NET DEN NO R KO R FIN
What is a Public Good?

Excludable Goods
 Rival Goods
Good
Excl.
Not Excl.
Rival
Tradable
Good
Natural
Resources
Not Rival
Natural
Monopoly
Public Good
Some Public Goods
 National Defense
 Public
Highways
 Health National System (Europe)
 Google?
 Primary Education
 Secondary Education
 Higher Education?
Comments on the Indicators

Public Expenditures = OECD indicator B4,
direct public expenditures on educational
institutions plus subsidies to households.
 Private expenditures = OECD indicator B2,
funding to educational institutions.
 Enrolment = gross enrolment ratio: actual
number enrolled as a percentage of the number
of youth in the official age group (World Bank
Data, The Economist)
Diversity Span
DIVERSITY SPAN = avg(4Q)-avg(1Q)
120
NOR
100
80
FIN
USA DEN SCA
JAP
UK
CAN AUS FRA
SWI
ITA
GER
SPA NET
60
40
20
0
NZE KOR
SWE
Countries under study
GDP
COUNTRY
20.34 USA
USA
6.46 Japan
JAP
3.96 Germany
GER
3.16 UK
UK
3.00 France
FRA
2.73 Italy
ITA
1.86 Spain
SPA
1.74 Canada
CAN
1.73 South Korea KOR
GDP
COUNTRY
1.33 Scandinavia SCA
1.05 Australia
AUS
0.88 Netherlands NET
0.46 Sweden
SWE
0.30 Norway
NOR
0.30 Denmark
DEN
0.27 Finland
FIN
0.15 New Zealand NZE
Compare only the best (2)

Let X be Australia
 GDP
(US)/GDP(AUS)=19.3
 ARWU(10th US University)=USX=12
 ARWU(Best(AUS))=54
 lag(AUS)=54-12=42

The Australian National University should gain
42 ranking positions to match the median of the
first 20 US universities
 Lag(AUS)/USX=42/12=3.5