Higher EducationHigher EducationSome International comparisonsSome International comparisons
Domingo DocampoDomingo DocampoUniversidade de Vigo (Spain)Universidade de Vigo (Spain)On sabbatical at ECE-UNMOn sabbatical at ECE-UNM
OutlineOutline of the Talk of the Talk
World Demand of Higher EducationWorld Demand of Higher Education The case of AustraliaThe case of Australia Two models of Higher Education FundingTwo models of Higher Education Funding OECD Indicators for the two modelsOECD Indicators for the two models How to tell the models apart?How to tell the models apart? ARWU data on researchARWU data on research Comparative performance of countries and US regionsComparative performance of countries and US regions Two conclusionsTwo conclusions
World’s demand of HEWorld’s demand of HE
Enrolment in Higher EducationEnrolment in Higher Education 97M students in 200097M students in 2000
263M in 2025 (predicted)263M in 2025 (predicted)
Mobility in Higher EducationMobility in Higher Education 1.9M foreign in 2000 (2%)1.9M foreign in 2000 (2%)
7.2M in 2025 (3%)7.2M in 2025 (3%)
World’s share of international World’s share of international students (2000-05)students (2000-05)
ITA (115)
SPA (116)
BEL (81)
NZE (600)
JAP (125)
CAN (82)
AUS (112)
FRA (123)
GER (98)
UK (95)
USA (85)
Mobility from Asia (1 million)Mobility from Asia (1 million)
USA (36)
UK (14)AUS (13)
JAP (11)
GER (9)
CAN (5)
FRA (4)
NZE (3)
KOR (1)
Asian mobility relative to GDPAsian mobility relative to GDP
NZE (38)
AUS (22)
UK (8)
CAN (6)
GER (4)
USA (3)
JAP (3)
FRA (2)
KOR (1)
Mobility to AustraliaMobility to Australia
INTERNATIONAL STUDENTS IN AUSTRALIA
95-9696-97
97-9899-00
00-01
01-02
02-03
03-04
04-0505-06
06-07
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
What What happened in Australiahappened in Australia??
Policy Reforms in 1987Policy Reforms in 1987Income-contingent loansIncome-contingent loansGovernment change in 1996Government change in 1996New Higher Education Act 2003New Higher Education Act 2003Changes in TuitionChanges in TuitionInternationalization of HEInternationalization of HE
On TuitionOn Tuition
If tuition was the answer, then what was the question?If tuition was the answer, then what was the question? Governments felt financially pressured, began to Governments felt financially pressured, began to
question whether higher education is a public good?question whether higher education is a public good? Private benefits do accrue to graduates.Private benefits do accrue to graduates. Positive externalities: Good citizens, Good taxpayers. Positive externalities: Good citizens, Good taxpayers. Debate in Australia 1986Debate in Australia 1986 New Zealand followed suitNew Zealand followed suit UK in 2003UK in 2003 Taboo in Continental EuropeTaboo in Continental Europe
The case for and againstThe case for and against Higher Education as a public good Higher Education as a public good
Education is a basic right Education is a basic right Graduates will return the benefits by paying more Graduates will return the benefits by paying more
taxes (around US$ 200,000 during a lifetime)taxes (around US$ 200,000 during a lifetime) Income tax is paid by many more non-graduates than Income tax is paid by many more non-graduates than
graduates: free higher education is horizontally graduates: free higher education is horizontally inequitablyinequitably
The taxpayer gets a good deal is a dangerous argument The taxpayer gets a good deal is a dangerous argument (R&D expenses)(R&D expenses)
Two modelsTwo models
Anglo-American modelAnglo-American model Encourages DiversityEncourages Diversity Heterogeneous InstitutionsHeterogeneous Institutions Quality comparisonsQuality comparisons
Scandinavian modelScandinavian model All programs ‘are’ equalAll programs ‘are’ equal Homogeneous InstitutionsHomogeneous Institutions Quality of a Public ServiceQuality of a Public Service
Two approaches to HE FundingTwo approaches to HE Funding
UtopianUtopian Very high taxesVery high taxes R&D commitmentR&D commitment High Public SpendingHigh Public Spending High EnrolmentHigh Enrolment
PracticalPractical Much lower taxesMuch lower taxes R&D commitmentR&D commitment High Private SpendingHigh Private Spending High EnrolmentHigh Enrolment
Are there utopian countries?Are there utopian countries?
Is there a way to tell a country apart?Is there a way to tell a country apart?Shouldn’t it be obvious?Shouldn’t it be obvious?Rationalize the obvious usingRationalize the obvious using
OECD dataOECD data OECD indicatorsOECD indicators The Economist and World Bank IndicatorsThe Economist and World Bank Indicators
Set of IndicatorsSet of Indicators
Taxes on Average worker (I5)Taxes on Average worker (I5)Enrolment (I6)Enrolment (I6)Percentage of GDP of:Percentage of GDP of:
Public expenditure on Education (I1)Public expenditure on Education (I1) Public expenditure on HE (I2)Public expenditure on HE (I2) Private expenditure on HE (I3)Private expenditure on HE (I3) Total spending on HE (I4)Total spending on HE (I4) Gross domestic expenditure on R&D (I7)Gross domestic expenditure on R&D (I7)
Main data TableMain data TableData Pub Edu Pub HE Priv HE Total HE Taxes Enrolment R&D
Country I1 I2 I3 I4 I5 I6 I7Australia 4.8 1.1 0.8 1.9 28.6 73.0 1.7Canada 5.0 1.7 1.0 2.7 32.3 58.0 2.0Denmark 8.3 2.5 0.1 2.6 41.5 67.0 2.6Finland 6.5 2.1 0.1 2.1 43.8 88.0 3.5France 5.9 1.2 0.2 1.4 47.4 56.0 2.2Germany 4.7 1.2 0.1 1.3 50.7 51.0 2.5Italy 4.9 0.8 0.2 1.0 45.7 57.0 1.2Japan 3.7 0.6 0.8 1.4 26.6 51.0 3.2Korea 4.6 0.6 2.0 2.6 16.6 85.0 2.6Netherlands 5.1 1.3 0.3 1.6 43.6 58.0 1.8Norway 7.6 2.3 0.1 2.4 36.9 81.0 1.8New Zealand 6.8 1.6 0.6 2.2 20.7 77.0 1.2Spain 4.3 1.0 0.3 1.2 38.0 62.0 1.1Sweden 7.5 2.2 0.2 2.3 48.0 83.0 4.0United Kingdom 5.4 1.1 0.3 1.4 31.2 64.0 1.9United States 5.7 1.5 1.6 3.1 29.6 83.0 2.7OECD average 5.5 1.3 0.4 1.7 36.5 63.0 2.3
Correlation MatrixCorrelation Matrix
Correlation Pub Edu Pub HE Priv HE Total HE Taxes Enrolment R&D
Pub Edu 1.00 0.90 -0.40 0.48 0.25 0.52 0.24Pub HE 1.00 -0.41 0.56 0.32 0.45 0.29Priv HE 1.00 0.53 -0.75 0.29 0.08
Total HE 1.00 -0.39 0.69 0.34Taxes 1.00 -0.32 0.19
Enrolment 1.00 0.19R&D 1.00
Total vs. Public ExpendituresTotal vs. Public Expenditures
FIGURE 2:TOTAL EXPENDITURES vs PUBLIC EXPENDITURES IN HIGHER EDUCATION
AUS
CAN
DENFIN
FRAGER
ITAJAP KOR
NET
NOR
NZE
SPA
SWE
UK
USA
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
TOTAL EXPENDITURES
PU
BL
IC E
XP
EN
DIT
UR
ES
Correlation=0.56
TAXES vs PRIVATE EXPENDITURESTAXES vs PRIVATE EXPENDITURES
FIGURE 4: TAXES ON AVERAGE WORKER vs PRIVATE EXPENDITURES IN HE
AUS
CAN
DEN
FIN
FRAGER
ITA
JAP
KOR
NET
NOR
NZE
SPA
SWE
UK
USA
TAXES ON AVERAGE WORKER
TO
TA
L E
XP
EN
DIT
UR
ES
HE
correlation=0.75
Total Expenditures in HETotal Expenditures in HEvs. Enrolmentvs. Enrolment
FIGURE 7: TOTAL EXPENDITURES IN HE vs ENROLMENT
ITA
KOR
CAN
SWE
FRA
AUSDEN
FIN
GERJAP
NET
SPA UK
NZE NOR USA
TOTAL EXPENDITURES IN HIGHER EDUCATION
EN
RO
LM
EN
T
correlation=0.69
PRINCIPAL COMPONENTSPRINCIPAL COMPONENTS
FACTOR ANALYSIS
USA
UK
SWE
SPA
NZE
NOR
NET
KOR
JAP
ITA
GER
FRA
FIN
DEN
CAN
AUS
-3 0 .0
-2 0 .0
-1 0 .0
0 .0
1 0 .0
2 0 .0
3 0 .0
4 0 .0
-3 0 -2 0 -1 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0
SECOND PRINCIPAL COMPONENT
FIR
ST
PR
INC
IPA
L C
OM
PO
NE
NT
PRINCIPAL COMPONENT 1PRINCIPAL COMPONENT 1FIRST PRINCIPAL COMPONENT
DENNOR
FIN
CAN
KORAUS
NET FRA UK
GER SPAITA
JAP
USANZE
SWE
47% OF THE VARIANCE EXPLAINED
PRINCIPAL COMPONENT 2PRINCIPAL COMPONENT 2
SECOND PRINCIPAL COMPONENT
CAN AUS JAP NZE
UK
SPANET NOR ITA FIN SWE FRA DEN GER
KOR
USA 40.5% OF THE VARIANCE EXPLAINED
Understanding the data Understanding the data
Normalize indicators: bNormalize indicators: best gets 100 pointsest gets 100 points Rearrange proportionallyRearrange proportionally Subtract OECD averageSubtract OECD average
Look at the sign of the correlationLook at the sign of the correlation I1, I2 and I5 correlate positively.I1, I2 and I5 correlate positively. I3 correlates negatively with them all.I3 correlates negatively with them all. I4 and I6 correlate positivelyI4 and I6 correlate positively
A measure for Utopia A measure for Utopia
M1 first principal component using only I1, I2, M1 first principal component using only I1, I2, I3 and I5I3 and I5
M2 first principal component using only I4, I6 M2 first principal component using only I4, I6 and I7and I7
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
Results of Measures M1 and M2
The new clusteringThe new clustering
Results of Indicators M1 and M2
USA
UK
SWE
SPA
NZE NOR
NET
KOR
JAPITA
GER
FRA
FIN
DENCAN
AUS
-20
-10
0
10
20
30
40
-60 -50 -40 -30 -20 -10 0 10 20 30 40
M1
M2
Correlation = 0.09
LANDING FROM UTOPIALANDING FROM UTOPIA
Principal component for indicators I1,I2,I3,I5
DEN
SWE
FIN
NET
ITA NZEUK SPA
CAN
AUSUSA
JAP
KOR
NOR
GERFRA
63% of the variance explained
LANDING FROM THE FUTURELANDING FROM THE FUTURE
Principal component for indicators I4,I6,I7
FIN KOR
DENNOR
CAN
NZEAUS
JAPUK NET FRA GER
SPA
ITA
USA SWE
-60
-40
-20
0
20
40
60
80
60% of the variance explained
Quality AssessmentQuality Assessment
Shanghai Jiao Tong University’s Academic Shanghai Jiao Tong University’s Academic Ranking of World Universities Ranking of World Universities
Based on Scientific ProductionBased on Scientific Production
Sound IndicatorsSound Indicators
Reliable DataReliable Data
Data can be Data can be aggregatedaggregated for countries for countries
Allows international comparisonsAllows international comparisons
It is not the whole story but…It is not the whole story but…
ARWUARWUWorld Rank
InstitutionScore on Alumni
Score on Award
Score on HiCi
Score on N&S
Score on SCI
Score on Size
Total Score
1 Harvard Univ 100 100 100 100 100 73.6 1002 Univ Cambridge 96.3 91.5 53.8 59.5 67.1 66.5 733 Stanford Univ 39.7 70.7 88.4 70 71.4 65.3 734 Univ California - Berkeley 70.6 74.5 70.5 72.2 71.9 53.1 725 Massachusetts Inst Tech (MIT) 72.9 80.6 66.6 66.4 62.2 53.6 706 California Inst Tech 57.1 69.1 59.1 64.5 50.1 100 667 Columbia Univ 78.2 59.4 56 53.6 69.8 45.8 628 Princeton Univ 61.1 75.3 59.6 43.5 47.3 58 598 Univ Chicago 72.9 80.2 49.9 43.7 54.1 41.8 5910 Univ Oxford 62 57.9 48 54.3 66 46 5811 Yale Univ 50.3 43.6 59.1 56.6 63 49.3 5612 Cornell Univ 44.9 51.3 56 48.4 65.2 40.1 5413 Univ California - San Diego 17.1 34 59.6 54.8 65.6 47.1 5114 Univ California - Los Angeles 26.4 32.1 57.6 47.5 77.3 34.9 5015 Univ Pennsylvania 34.2 34.4 57 41.7 73.6 40 5016 Univ Wisconsin - Madison 41.5 35.5 53.3 45.1 68.3 29.3 4917 Univ Washington - Seattle 27.7 31.8 53.3 47.6 75.5 27.8 4918 Univ California - San Francisco 0 36.8 55.5 54.8 61.1 48.2 4819 Tokyo Univ 34.8 14.1 41.4 51.5 85.5 35.2 4720 Johns Hopkins Univ 49.5 27.8 40.7 52.2 68.8 25.3 4721 Univ Michigan - Ann Arbor 41.5 0 61.5 41.6 76.9 31.2 4522 Kyoto Univ 38.3 33.4 36.9 36.2 72.4 31.7 4423 Imperial College London 20.1 37.4 40 39.7 64.2 40.2 4324 Univ Toronto 27.1 19.3 38.5 36.5 78.3 44.8 4325 Univ Illinois - Urbana Champaign 40.1 36.6 45.5 33.6 57.7 26.3 43
CORRELATION MATRIXCORRELATION 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 BETTERHOW GOOD ARE THE BETTER
RATIO avg(4Q)/avg(1Q)
USA
SWIUK DEN
NORFIN JAP
CANSCA FRA NET SWE AUS GER
ITASPA KOR NZE
0
20
40
60
80
100
120
Cutting the US in Cutting the US in European like slicesEuropean like slices
Regions GDP %MID 2,645 4.34STH 2,093 3.43EAS 1,994 3.27CA 1,655 2.71WST 1,426 2.34NY 975 1.60TX 951 1.56FL 670 1.10
USA TOTAL 12,409 20.34
GDP in millions
How good are the better nowHow good are the better now
RATIO avg(4Q)/avg(1Q)
CA
NY
USAEAS
MID
UK WST FL TX JAP CAN STH SCA FRA NET AUS GERITA SPA KOR
0
20
40
60
80
100
120
Compare only the best universityCompare only the best university
Given a REGION X, let N(X) be equal toGiven a REGION X, let N(X) be equal to
GDP(US)/GDP(X)GDP(US)/GDP(X)Let USX be the median of the first N(X) US Let USX be the median of the first N(X) US
universities’ rank. universities’ rank. Let Lag(X) be the difference between the rank Let Lag(X) be the difference between the rank
of the best university from region X and USX.of the best university from region X and USX.
Normalize the result Normalize the result lag(X)/USXlag(X)/USX
Prima Donna (1)Prima Donna (1)
LAG(X)/USX
UK
SWI USA SIN DEN ISR FIN SWE NOR NZENET CAN
AUSBEL AUT
GER
SPA
KOR
JAP
ITA
FRA
-5.0
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Prima Donna (2)Prima Donna (2)
LAG(X)/USX
EASUK CA
USA NY
MIDNET
WST CAN
AUSTX FL
SCA
-2
-1
0
1
2
3
4
5
Prima Donna (3)Prima Donna (3)
LAG(X)/USX
JAP STHFRA GER
ITA
SPA
KOR
CHI
0
10
20
30
40
50
60
70
80
90
Universities in ARWUUniversities in ARWU
25 50 100 200 500 25 50 100 200 500 25 50 100 200 500USA 5 10 20 41 102 19 37 54 87 167 374 364 265 214 164CA 1 1 3 5 14 6 10 10 12 14 885 738 369 221 103MID 1 2 4 9 22 4 6 12 17 27 369 277 277 196 125EAS 1 1 2 4 11 5 9 14 18 38 612 551 428 275 233NY 1 2 3 8 3 5 6 8 15 751 626 375 250 188UK 1 2 3 6 16 3 5 11 22 43 380 317 348 348 272JAP 2 3 6 13 32 2 2 6 9 32 124 62 93 70 99CAN 1 2 3 9 1 2 4 8 22 230 230 230 230 253WST 1 1 3 5 13 1 3 6 10 24 156 234 234 195 187STH 1 2 3 7 17 2 3 13 32 117 87 189 187TX 1 2 3 8 2 2 7 13 257 128 225 167SCA 1 1 3 7 1 7 9 25 150 525 338 375FRA 1 1 3 6 15 1 4 6 21 67 133 100 140NET 1 2 5 1 2 7 12 227 397 272GER 1 2 4 8 20 5 15 40 126 189 202AUS 1 1 2 5 2 6 16 190 285 304ITA 1 1 3 5 14 1 6 23 37 110 168FL 1 1 2 5 1 2 4 91 91 73
Number of Universities by share of GDP Actual number of universities Ratio actual/expected
HOW GOOD ARE THE BETTER?HOW GOOD ARE THE BETTER?
SLOPE ACROSS ARWU
CA
SWI
NY
EAS
MID ISRUSA DEN
SIN BELSWE FIN NOR TX UK
WEST
JAP FL NET CHICAN FRA STHSPA GER AUS ITA KOR
CLUSTERING (25-500)CLUSTERING (25-500)
Principal components clustering (94% of the variance explained)
SPA
KOR
ITA
AUS
FL
GERNET
FRA
SCA
TXSTH
CANWST
JAP
EAS
NY
UK
MID
CA
USA
RANKING ACROSS ARWURANKING ACROSS ARWU
ACROSS ARWU First Principal Component (63% of the variance explained)
CANY
EAS
UK
SCA USAMID
CAN NET WST
TX AUS
STHGER
FRA JAP
ITA FL
KOR SPA
From 50 to 500From 50 to 500
Best 50: First Principal Component (68% of the variance explained)
SCAEAS NY CA
UK
NET
USACAN WST
MID AUSTX
STHGER
FRA
JAP ITAFL
KOR SPA
From 100 to 500From 100 to 500
Best 100 Principal Component (81% of the variance explained)
SCA
UKNET EAS
NY AUSCAN CA WST USA
MIDTX GER
STH
FRAITA
JAP FL
KOR SPA
Over-share of GDP (500) Over-share of GDP (500)
Over-representation in arwu (500)
SCA
AUS
UK NETCAN
EAS
WST GER STHNY
ITA TX USA
FRA
MIDKOR CA JAP SPA
FL
BEST 500 (GDP SHARE)BEST 500 (GDP SHARE)
AVERAGE RANKING OF THE BEST (GDP SHARE) UNIVERSITIES ON ARWU 500 (FIRST TIER)
CA (14)
SCA (7) UK (16)NET (4) TX (8)
MID (22)
CAN (9)WST (13)
GER (20)
FL (5)
FRA (15)
NY (8)EAS (16)
USA (102)AUS (5)
STH (17)
0
50
100
150
200
250
CORRELATION MATRIXCORRELATION MATRIX
Correlation ALU STAFF HICI S&N SCI SCORE
ALU 1.00 0.96 0.68 0.80 0.50 0.90
STAFF 1.00 0.67 0.79 0.44 0.89
HICI 1.00 0.96 0.74 0.91
S&N 1.00 0.74 0.97
SCI 1.00 0.75
SCORE 1.00
BEST 500BIG COUNTRIES
Sci (500)Sci (500)
SCI SCORE BEST 500 (AVERAGE SHARE GDP REGIONS)
CA CANUK AUS MID
NET USA NY SCASWE
STH WST ISR SWI FL TX KOR EAS GERITA
JAP SPA
FRA
0
20
40
60
80
100
120
Quality vs Quantity (500)Quality vs Quantity (500)
SCI vs S&N
UK
ITA JAP
USA
KOR
CANAUS
FRA
SCA
GER
SPA
NET
0
20
40
60
80
100
120
0 20 40 60 80 100 120
S&N
SC
I
CORRELATION= 0.74
Quality IndicatorsQuality Indicators
HICI vs S&N (500) Large Countries
UK
ITAJAP
USA
KOR
CANAUS
FRA
SCA
GER
SPA
NET
HiCi
S&N
CORRELATION= 0.96
Conclusions (1)Conclusions (1)
There are indeed two models to There are indeed two models to properly fund Higher Educationproperly fund Higher Education
Choose one, but please, to the Choose one, but please, to the fullest.fullest.
Conclusions (2)Conclusions (2)
Benchmarking is a good basis for Benchmarking is a good basis for improvement. Through international improvement. Through international benchmarking countries can identify best benchmarking countries can identify best practices and ways forward. practices and ways forward.
Identify the appropriate incentives to Identify the appropriate incentives to encourage and reward excellence.encourage and reward excellence.
Conclusions (3)Conclusions (3)
Galbraith once said that given the Galbraith once said that given the choice of proving that changes are choice of proving that changes are unnecessary, most people…unnecessary, most people…
Please, do not.Please, do not.THANKSTHANKS
HiCi (500)HiCi (500)
HiCi SCORE BEST 500 (AVERAGE SHARE GDP REGIONS)
CA
EASNY
USA
MID
WST UK TXSWI
STH FL CAN AUS SWE SCA NET ISR
JAP GERITA FRA
SPAKOR
0
20
40
60
80
100
120
S&N (500)S&N (500)
S&N SCORE BEST 500 (AVERAGE SHARE GDP REGIONS)
CA
NY
SWIUK WST EAS USA
TXMID NET
SCA SWE CAN ISRAUS
STHGER
FRA FLJAP
ITASPA KOR
0
20
40
60
80
100
120
ACROSS ARWUACROSS ARWU
REGIONS 25 50 100 200 500 INDEXCA 3 3 4 8 58 1,442EAS 1 3 7 17 63 1,426UK 2 5 12 29 76 1,393NY 7 7 7 10 65 1,384USA 4 7 13 28 104 1,372MID 8 13 19 37 126 1,255WST 17 19 24 47 113 1,072CAN 24 24 29 48 113 1,044TX 38 38 56 121 968NET 40 40 54 96 956STH 35 51 82 168 887SCA 48 50 56 68 843JAP 20 37 64 126 290 785AUS 55 70 117 784FRA 51 68 103 212 716FL 62 111 206 702GER 65 91 156 670
A B C D E
index =5*( 25-A)+4*(50-B)+3*(100-C)+2*(200-D)+500-EAVERAGE RANKING OF THE BEST UNIVERSITIES ACCORDING TO GDP SHARE
ACROSS ARWU (2)ACROSS ARWU (2)
NORMALIZED INDEX
CA EAS UK NY USA
MID
WST CANTX NET
STHSCA
JAP AUSFRA FL GER
0
20
40
60
80
100
120
International Students (2)International Students (2)TO OECD COUNTRIES BY ORIGIN (thousands)
2004? (90)
China (330)
India (120)
Africa (250)
Asia (540)
Europe (570)
N Am (80)
Oceania (20)
S Am (135)
International Students International Students
BY DESTINY 2004
USA
UKGER
FRA
AUSCAN JAP
NZEBEL SPA ITA SWE SWI AUT NET DEN NO R KO R FIN
0
100000
200000
300000
400000
500000
600000
700000
What is a Public Good?What is a Public Good?Excludable GoodsExcludable GoodsRival GoodsRival Goods
GoodGood Excl.Excl. Not Excl.Not Excl.
RivalRival Tradable Tradable GoodGood
Natural Natural ResourcesResources
Not Rival Not Rival Natural Natural MonopolyMonopoly
Public Public GoodGood
Some Public GoodsSome Public Goods
National DefenseNational DefensePublic HighwaysPublic HighwaysHealth National System (Europe)Health National System (Europe)Google?Google?Primary EducationPrimary EducationSecondary EducationSecondary EducationHigher Education?Higher Education?
Comments on the IndicatorsComments on the Indicators
Public Expenditures = OECD indicator B4, Public Expenditures = OECD indicator B4, direct public expenditures on educational direct public expenditures on educational institutions plus subsidies to households. institutions plus subsidies to households.
Private expenditures = OECD indicator B2, Private expenditures = OECD indicator B2, funding to educational institutions.funding to educational institutions.
Enrolment = gross enrolment ratio: actual Enrolment = gross enrolment ratio: actual number enrolled as a percentage of the number number enrolled as a percentage of the number of youth in the official age group (World Bank of youth in the official age group (World Bank Data, The Economist)Data, The Economist)
Diversity SpanDiversity Span
DIVERSITY SPAN = avg(4Q)-avg(1Q)
NORFIN
USA DEN SCAJAP
UK CAN AUS FRASWI ITA
GERSPA NET
NZE KOR SWE
0
20
40
60
80
100
120
Countries under studyCountries under study
GDP GDP20.34 USA USA 1.33 Scandinavia SCA6.46 Japan JAP 1.05 Australia AUS3.96 Germany GER 0.88 Netherlands NET3.16 UK UK 0.46 Sweden SWE3.00 France FRA 0.30 Norway NOR2.73 Italy ITA 0.30 Denmark DEN1.86 Spain SPA 0.27 Finland FIN1.74 Canada CAN 0.15 New Zealand NZE1.73 South Korea KOR
COUNTRY COUNTRY
Compare only the best (2)Compare only the best (2)
Let X be AustraliaLet X be Australia GDP (US)/GDP(AUS)=19.3GDP (US)/GDP(AUS)=19.3 ARWU(10th US University)=USX=12ARWU(10th US University)=USX=12 ARWU(Best(AUS))=54ARWU(Best(AUS))=54 lag(AUS)=54-12=42lag(AUS)=54-12=42
The Australian National University should gain The Australian National University should gain 42 ranking positions to match the median of the 42 ranking positions to match the median of the first 20 US universitiesfirst 20 US universities
Lag(AUS)/USX=42/12=3.5Lag(AUS)/USX=42/12=3.5