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